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name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.20989">arXiv:2407.20989</a> <span> [<a href="https://arxiv.org/pdf/2407.20989">pdf</a>, <a href="https://arxiv.org/format/2407.20989">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gauy%2C+M+M">Marcelo Matheus Gauy</a>, <a href="/search/cs?searchtype=author&query=Koza%2C+N+H">Natalia Hitomi Koza</a>, <a href="/search/cs?searchtype=author&query=Morita%2C+R+M">Ricardo Mikio Morita</a>, <a href="/search/cs?searchtype=author&query=Stanzione%2C+G+R">Gabriel Rocha Stanzione</a>, <a href="/search/cs?searchtype=author&query=Junior%2C+A+C">Arnaldo Candido Junior</a>, <a href="/search/cs?searchtype=author&query=Berti%2C+L+C">Larissa Cristina Berti</a>, <a href="/search/cs?searchtype=author&query=Levin%2C+A+S+S">Anna Sara Shafferman Levin</a>, <a href="/search/cs?searchtype=author&query=Sabino%2C+E+C">Ester Cerdeira Sabino</a>, <a href="/search/cs?searchtype=author&query=Svartman%2C+F+R+F">Flaviane Romani Fernandes Svartman</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.20989v1-abstract-short" style="display: inline;"> We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analys… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20989v1-abstract-full').style.display = 'inline'; document.getElementById('2407.20989v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.20989v1-abstract-full" style="display: none;"> We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20989v1-abstract-full').style.display = 'none'; document.getElementById('2407.20989v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 4 figures, in review at Journal of Biomedical Signal Processing and Control</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.17569">arXiv:2405.17569</a> <span> [<a href="https://arxiv.org/pdf/2405.17569">pdf</a>, <a href="https://arxiv.org/format/2405.17569">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-031-34344-5_32">10.1007/978-3-031-34344-5_32 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gauy%2C+M+M">Marcelo Matheus Gauy</a>, <a href="/search/cs?searchtype=author&query=Berti%2C+L+C">Larissa Cristina Berti</a>, <a href="/search/cs?searchtype=author&query=C%C3%A2ndido%2C+A">Arnaldo C芒ndido Jr</a>, <a href="/search/cs?searchtype=author&query=Neto%2C+A+C">Augusto Camargo Neto</a>, <a href="/search/cs?searchtype=author&query=Goldman%2C+A">Alfredo Goldman</a>, <a href="/search/cs?searchtype=author&query=Levin%2C+A+S+S">Anna Sara Shafferman Levin</a>, <a href="/search/cs?searchtype=author&query=Martins%2C+M">Marcus Martins</a>, <a href="/search/cs?searchtype=author&query=de+Medeiros%2C+B+R">Beatriz Raposo de Medeiros</a>, <a href="/search/cs?searchtype=author&query=Queiroz%2C+M">Marcelo Queiroz</a>, <a href="/search/cs?searchtype=author&query=Sabino%2C+E+C">Ester Cerdeira Sabino</a>, <a href="/search/cs?searchtype=author&query=Svartman%2C+F+R+F">Flaviane Romani Fernandes Svartman</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.17569v1-abstract-short" style="display: inline;"> This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI dete… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17569v1-abstract-full').style.display = 'inline'; document.getElementById('2405.17569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.17569v1-abstract-full" style="display: none;"> This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17569v1-abstract-full').style.display = 'none'; document.getElementById('2405.17569v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 2 figures, 1 table. Published in Artificial Intelligence in Medicine (AIME) 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Artificial Intellingence in Medicine Proceedings 2023, page 271-275 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.19204">arXiv:2402.19204</a> <span> [<a href="https://arxiv.org/pdf/2402.19204">pdf</a>, <a href="https://arxiv.org/format/2402.19204">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> PeLLE: Encoder-based language models for Brazilian Portuguese based on open data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=de+Mello%2C+G+L">Guilherme Lamartine de Mello</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a>, <a href="/search/cs?searchtype=author&query=Serras%2C+a+F">and Felipe Serras</a>, <a href="/search/cs?searchtype=author&query=Carpi%2C+M+d+M">Miguel de Mello Carpi</a>, <a href="/search/cs?searchtype=author&query=Jose%2C+M+M">Marcos Menon Jose</a>, <a href="/search/cs?searchtype=author&query=Domingues%2C+P+H">Pedro Henrique Domingues</a>, <a href="/search/cs?searchtype=author&query=Cavalim%2C+P">Paulo Cavalim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.19204v1-abstract-short" style="display: inline;"> In this paper we present PeLLE, a family of large language models based on the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open data from the Carolina corpus. Aiming at reproducible results, we describe details of the pretraining of the models. We also evaluate PeLLE models against a set of existing multilingual and PT-BR refined pretrained Transformer-based LLM encoders, c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.19204v1-abstract-full').style.display = 'inline'; document.getElementById('2402.19204v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.19204v1-abstract-full" style="display: none;"> In this paper we present PeLLE, a family of large language models based on the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open data from the Carolina corpus. Aiming at reproducible results, we describe details of the pretraining of the models. We also evaluate PeLLE models against a set of existing multilingual and PT-BR refined pretrained Transformer-based LLM encoders, contrasting performance of large versus smaller-but-curated pretrained models in several downstream tasks. We conclude that several tasks perform better with larger models, but some tasks benefit from smaller-but-curated data in its pretraining. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.19204v1-abstract-full').style.display = 'none'; document.getElementById('2402.19204v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.09265">arXiv:2312.09265</a> <span> [<a href="https://arxiv.org/pdf/2312.09265">pdf</a>, <a href="https://arxiv.org/ps/2312.09265">ps</a>, <a href="https://arxiv.org/format/2312.09265">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Acoustic models of Brazilian Portuguese Speech based on Neural Transformers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gauy%2C+M+M">Marcelo Matheus Gauy</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.09265v1-abstract-short" style="display: inline;"> An acoustic model, trained on a significant amount of unlabeled data, consists of a self-supervised learned speech representation useful for solving downstream tasks, perhaps after a fine-tuning of the model in the respective downstream task. In this work, we build an acoustic model of Brazilian Portuguese Speech through a Transformer neural network. This model was pretrained on more than $800$ ho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09265v1-abstract-full').style.display = 'inline'; document.getElementById('2312.09265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.09265v1-abstract-full" style="display: none;"> An acoustic model, trained on a significant amount of unlabeled data, consists of a self-supervised learned speech representation useful for solving downstream tasks, perhaps after a fine-tuning of the model in the respective downstream task. In this work, we build an acoustic model of Brazilian Portuguese Speech through a Transformer neural network. This model was pretrained on more than $800$ hours of Brazilian Portuguese Speech, using a combination of pretraining techniques. Using a labeled dataset collected for the detection of respiratory insufficiency in Brazilian Portuguese speakers, we fine-tune the pretrained Transformer neural network on the following tasks: respiratory insufficiency detection, gender recognition and age group classification. We compare the performance of pretrained Transformers on these tasks with that of Transformers without previous pretraining, noting a significant improvement. In particular, the performance of respiratory insufficiency detection obtains the best reported results so far, indicating this kind of acoustic model as a promising tool for speech-as-biomarker approach. Moreover, the performance of gender recognition is comparable to the state of the art models in English. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.09265v1-abstract-full').style.display = 'none'; document.getElementById('2312.09265v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under review at Journal of Brazilian Computer Society</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.08593">arXiv:2307.08593</a> <span> [<a href="https://arxiv.org/pdf/2307.08593">pdf</a>, <a href="https://arxiv.org/format/2307.08593">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Theory">nucl-th</span> </div> </div> <p class="title is-5 mathjax"> Artificial Intelligence for the Electron Ion Collider (AI4EIC) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Allaire%2C+C">C. Allaire</a>, <a href="/search/cs?searchtype=author&query=Ammendola%2C+R">R. Ammendola</a>, <a href="/search/cs?searchtype=author&query=Aschenauer%2C+E+-">E. -C. Aschenauer</a>, <a href="/search/cs?searchtype=author&query=Balandat%2C+M">M. Balandat</a>, <a href="/search/cs?searchtype=author&query=Battaglieri%2C+M">M. Battaglieri</a>, <a href="/search/cs?searchtype=author&query=Bernauer%2C+J">J. Bernauer</a>, <a href="/search/cs?searchtype=author&query=Bond%C3%AC%2C+M">M. Bond矛</a>, <a href="/search/cs?searchtype=author&query=Branson%2C+N">N. Branson</a>, <a href="/search/cs?searchtype=author&query=Britton%2C+T">T. Britton</a>, <a href="/search/cs?searchtype=author&query=Butter%2C+A">A. Butter</a>, <a href="/search/cs?searchtype=author&query=Chahrour%2C+I">I. Chahrour</a>, <a href="/search/cs?searchtype=author&query=Chatagnon%2C+P">P. Chatagnon</a>, <a href="/search/cs?searchtype=author&query=Cisbani%2C+E">E. Cisbani</a>, <a href="/search/cs?searchtype=author&query=Cline%2C+E+W">E. W. Cline</a>, <a href="/search/cs?searchtype=author&query=Dash%2C+S">S. Dash</a>, <a href="/search/cs?searchtype=author&query=Dean%2C+C">C. Dean</a>, <a href="/search/cs?searchtype=author&query=Deconinck%2C+W">W. Deconinck</a>, <a href="/search/cs?searchtype=author&query=Deshpande%2C+A">A. Deshpande</a>, <a href="/search/cs?searchtype=author&query=Diefenthaler%2C+M">M. Diefenthaler</a>, <a href="/search/cs?searchtype=author&query=Ent%2C+R">R. Ent</a>, <a href="/search/cs?searchtype=author&query=Fanelli%2C+C">C. Fanelli</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">M. Finger</a>, <a href="/search/cs?searchtype=author&query=Finger%2C%2C+M">M. Finger, Jr.</a>, <a href="/search/cs?searchtype=author&query=Fol%2C+E">E. Fol</a>, <a href="/search/cs?searchtype=author&query=Furletov%2C+S">S. Furletov</a> , et al. (70 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.08593v1-abstract-short" style="display: inline;"> The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.08593v1-abstract-full').style.display = 'inline'; document.getElementById('2307.08593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.08593v1-abstract-full" style="display: none;"> The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.08593v1-abstract-full').style.display = 'none'; document.getElementById('2307.08593v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 11 figures, AI4EIC workshop, tutorials and hackathon</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.16098">arXiv:2303.16098</a> <span> [<a href="https://arxiv.org/pdf/2303.16098">pdf</a>, <a href="https://arxiv.org/format/2303.16098">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Crespo%2C+M+C+R+M">Maria Clara Ramos Morales Crespo</a>, <a href="/search/cs?searchtype=author&query=Rocha%2C+M+L+d+S+J">Maria Lina de Souza Jeannine Rocha</a>, <a href="/search/cs?searchtype=author&query=Sturzeneker%2C+M+L">Mariana Louren莽o Sturzeneker</a>, <a href="/search/cs?searchtype=author&query=Serras%2C+F+R">Felipe Ribas Serras</a>, <a href="/search/cs?searchtype=author&query=de+Mello%2C+G+L">Guilherme Lamartine de Mello</a>, <a href="/search/cs?searchtype=author&query=Costa%2C+A+S">Aline Silva Costa</a>, <a href="/search/cs?searchtype=author&query=Palma%2C+M+F">Mayara Feliciano Palma</a>, <a href="/search/cs?searchtype=author&query=Mesquita%2C+R+M">Renata Morais Mesquita</a>, <a href="/search/cs?searchtype=author&query=Guets%2C+R+d+P">Raquel de Paula Guets</a>, <a href="/search/cs?searchtype=author&query=da+Silva%2C+M+M">Mariana Marques da Silva</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a>, <a href="/search/cs?searchtype=author&query=de+Sousa%2C+M+C+P">Maria Clara Paix茫o de Sousa</a>, <a href="/search/cs?searchtype=author&query=Namiuti%2C+C">Cristiane Namiuti</a>, <a href="/search/cs?searchtype=author&query=Monte%2C+V+M+d">Vanessa Martins do Monte</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.16098v1-abstract-short" style="display: inline;"> This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with provenance, typology, versioning, and text integrality. The corpus aims at being used both as a reliable source for research in Linguistics and as an import… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16098v1-abstract-full').style.display = 'inline'; document.getElementById('2303.16098v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.16098v1-abstract-full" style="display: none;"> This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with provenance, typology, versioning, and text integrality. The corpus aims at being used both as a reliable source for research in Linguistics and as an important resource for Computer Science research on language models, contributing towards removing Portuguese from the set of low-resource languages. Here we present the construction of the corpus methodology, comparing it with other existing methodologies, as well as the corpus current state: Carolina's first public version has $653,322,577$ tokens, distributed over $7$ broad types. Each text is annotated with several different metadata categories in its header, which we developed using TEI annotation standards. We also present ongoing derivative works and invite NLP researchers to contribute with their own. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16098v1-abstract-full').style.display = 'none'; document.getElementById('2303.16098v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 3 figures, 1 appendix</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T50 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.14372">arXiv:2211.14372</a> <span> [<a href="https://arxiv.org/pdf/2211.14372">pdf</a>, <a href="https://arxiv.org/format/2211.14372">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Interpretability Analysis of Deep Models for COVID-19 Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=da+Silva%2C+D+P+P">Daniel Peixoto Pinto da Silva</a>, <a href="/search/cs?searchtype=author&query=Casanova%2C+E">Edresson Casanova</a>, <a href="/search/cs?searchtype=author&query=Gris%2C+L+R+S">Lucas Rafael Stefanel Gris</a>, <a href="/search/cs?searchtype=author&query=Junior%2C+A+C">Arnaldo Candido Junior</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a>, <a href="/search/cs?searchtype=author&query=Svartman%2C+F">Flaviane Svartman</a>, <a href="/search/cs?searchtype=author&query=Raposo%2C+B">Beatriz Raposo</a>, <a href="/search/cs?searchtype=author&query=Martins%2C+M+V+M">Marcus Vin铆cius Moreira Martins</a>, <a href="/search/cs?searchtype=author&query=Alu%C3%ADsio%2C+S+M">Sandra Maria Alu铆sio</a>, <a href="/search/cs?searchtype=author&query=Berti%2C+L+C">Larissa Cristina Berti</a>, <a href="/search/cs?searchtype=author&query=Teixeira%2C+J+P">Jo茫o Paulo Teixeira</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.14372v1-abstract-short" style="display: inline;"> During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection in audios. We investigate which features are important for model decision process, investigating spectrograms, F0, F0 standard deviation, sex and age.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14372v1-abstract-full').style.display = 'inline'; document.getElementById('2211.14372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.14372v1-abstract-full" style="display: none;"> During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection in audios. We investigate which features are important for model decision process, investigating spectrograms, F0, F0 standard deviation, sex and age. Following, we analyse model decisions by generating heat maps for the trained models to capture their attention during the decision process. Focusing on a explainable Inteligence Artificial approach, we show that studied models can taken unbiased decisions even in the presence of spurious data in the training set, given the adequate preprocessing steps. Our best model has 94.44% of accuracy in detection, with results indicating that models favors spectrograms for the decision process, particularly, high energy areas in the spectrogram related to prosodic domains, while F0 also leads to efficient COVID-19 detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14372v1-abstract-full').style.display = 'none'; document.getElementById('2211.14372v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.14716">arXiv:2210.14716</a> <span> [<a href="https://arxiv.org/pdf/2210.14716">pdf</a>, <a href="https://arxiv.org/format/2210.14716">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Pretrained audio neural networks for Speech emotion recognition in Portuguese </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gauy%2C+M+M">Marcelo Matheus Gauy</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.14716v1-abstract-short" style="display: inline;"> The goal of speech emotion recognition (SER) is to identify the emotional aspects of speech. The SER challenge for Brazilian Portuguese speech was proposed with short snippets of Portuguese which are classified as neutral, non-neutral female and non-neutral male according to paralinguistic elements (laughing, crying, etc). This dataset contains about $50$ minutes of Brazilian Portuguese speech. As… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14716v1-abstract-full').style.display = 'inline'; document.getElementById('2210.14716v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.14716v1-abstract-full" style="display: none;"> The goal of speech emotion recognition (SER) is to identify the emotional aspects of speech. The SER challenge for Brazilian Portuguese speech was proposed with short snippets of Portuguese which are classified as neutral, non-neutral female and non-neutral male according to paralinguistic elements (laughing, crying, etc). This dataset contains about $50$ minutes of Brazilian Portuguese speech. As the dataset leans on the small side, we investigate whether a combination of transfer learning and data augmentation techniques can produce positive results. Thus, by combining a data augmentation technique called SpecAugment, with the use of Pretrained Audio Neural Networks (PANNs) for transfer learning we are able to obtain interesting results. The PANNs (CNN6, CNN10 and CNN14) are pretrained on a large dataset called AudioSet containing more than $5000$ hours of audio. They were finetuned on the SER dataset and the best performing model (CNN10) on the validation set was submitted to the challenge, achieving an $F1$ score of $0.73$ up from $0.54$ from the baselines provided by the challenge. Moreover, we also tested the use of Transformer neural architecture, pretrained on about $600$ hours of Brazilian Portuguese audio data. Transformers, as well as more complex models of PANNs (CNN14), fail to generalize to the test set in the SER dataset and do not beat the baseline. Considering the limitation of the dataset sizes, currently the best approach for SER is using PANNs (specifically, CNN6 and CNN10). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14716v1-abstract-full').style.display = 'none'; document.getElementById('2210.14716v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> First Workshop on Automatic Speech Recognition for Spontaneous and Prepared Speech Speech emotion recognition in Portuguese (SER 2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.14085">arXiv:2210.14085</a> <span> [<a href="https://arxiv.org/pdf/2210.14085">pdf</a>, <a href="https://arxiv.org/format/2210.14085">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5753/stil.2021.17793">10.5753/stil.2021.17793 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gauy%2C+M+M">Marcelo Matheus Gauy</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.14085v1-abstract-short" style="display: inline;"> This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an accuracy of $87.04\%$, validating the hypothesis that one can detect RI through spe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14085v1-abstract-full').style.display = 'inline'; document.getElementById('2210.14085v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.14085v1-abstract-full" style="display: none;"> This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an accuracy of $87.04\%$, validating the hypothesis that one can detect RI through speech. Here, we study how Transformer neural network architectures can improve the performance on RI detection. This approach enables construction of an acoustic model. By choosing the correct pretraining technique, we generate a self-supervised acoustic model, leading to improved performance ($96.53\%$) of Transformers for RI detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14085v1-abstract-full').style.display = 'none'; document.getElementById('2210.14085v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> SIMP脫SIO BRASILEIRO DE TECNOLOGIA DA INFORMA脟脙O E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computa莽茫o, 2021 . p. 143-152 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.09185">arXiv:2205.09185</a> <span> [<a href="https://arxiv.org/pdf/2205.09185">pdf</a>, <a href="https://arxiv.org/format/2205.09185">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2022.167748">10.1016/j.nima.2022.167748 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fanelli%2C+C">C. Fanelli</a>, <a href="/search/cs?searchtype=author&query=Papandreou%2C+Z">Z. Papandreou</a>, <a href="/search/cs?searchtype=author&query=Suresh%2C+K">K. Suresh</a>, <a href="/search/cs?searchtype=author&query=Adkins%2C+J+K">J. K. Adkins</a>, <a href="/search/cs?searchtype=author&query=Akiba%2C+Y">Y. Akiba</a>, <a href="/search/cs?searchtype=author&query=Albataineh%2C+A">A. Albataineh</a>, <a href="/search/cs?searchtype=author&query=Amaryan%2C+M">M. Amaryan</a>, <a href="/search/cs?searchtype=author&query=Arsene%2C+I+C">I. C. Arsene</a>, <a href="/search/cs?searchtype=author&query=Gayoso%2C+C+A">C. Ayerbe Gayoso</a>, <a href="/search/cs?searchtype=author&query=Bae%2C+J">J. Bae</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/cs?searchtype=author&query=Baker%2C+M+D">M. D. Baker</a>, <a href="/search/cs?searchtype=author&query=Bashkanov%2C+M">M. Bashkanov</a>, <a href="/search/cs?searchtype=author&query=Bellwied%2C+R">R. Bellwied</a>, <a href="/search/cs?searchtype=author&query=Benmokhtar%2C+F">F. Benmokhtar</a>, <a href="/search/cs?searchtype=author&query=Berdnikov%2C+V">V. Berdnikov</a>, <a href="/search/cs?searchtype=author&query=Bernauer%2C+J+C">J. C. Bernauer</a>, <a href="/search/cs?searchtype=author&query=Bock%2C+F">F. Bock</a>, <a href="/search/cs?searchtype=author&query=Boeglin%2C+W">W. Boeglin</a>, <a href="/search/cs?searchtype=author&query=Borysova%2C+M">M. Borysova</a>, <a href="/search/cs?searchtype=author&query=Brash%2C+E">E. Brash</a>, <a href="/search/cs?searchtype=author&query=Brindza%2C+P">P. Brindza</a>, <a href="/search/cs?searchtype=author&query=Briscoe%2C+W+J">W. J. Briscoe</a>, <a href="/search/cs?searchtype=author&query=Brooks%2C+M">M. Brooks</a>, <a href="/search/cs?searchtype=author&query=Bueltmann%2C+S">S. Bueltmann</a> , et al. (258 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.09185v2-abstract-short" style="display: inline;"> The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.09185v2-abstract-full').style.display = 'inline'; document.getElementById('2205.09185v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.09185v2-abstract-full" style="display: none;"> The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.09185v2-abstract-full').style.display = 'none'; document.getElementById('2205.09185v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 18 figures, 2 appendices, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.00361">arXiv:2205.00361</a> <span> [<a href="https://arxiv.org/pdf/2205.00361">pdf</a>, <a href="https://arxiv.org/format/2205.00361">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Combined Learning of Neural Network Weights for Privacy in Collaborative Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ioste%2C+A+R">Aline R. Ioste</a>, <a href="/search/cs?searchtype=author&query=Durham%2C+A+M">Alan M. Durham</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.00361v1-abstract-short" style="display: inline;"> We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameters to train a model using only locally available data. Locally trained models are then submitted to a combining agent, which produces a combined model.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00361v1-abstract-full').style.display = 'inline'; document.getElementById('2205.00361v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.00361v1-abstract-full" style="display: none;"> We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameters to train a model using only locally available data. Locally trained models are then submitted to a combining agent, which produces a combined model. The new model's parameters can be sent back to hosts, and can then be used as initial parameters for a new training iteration. CoLN is capable of combining several distributed neural networks of the same kind but is not restricted to any single neural architecture. In this paper we detail the combination algorithm and present experiments with feed-forward, convolutional, and recurrent Neural Network architectures, showing that the CoLN combined model approximates the performance of a hypothetical ideal centralized model, trained using the combination of the local datasets. CoLN can contribute to secure collaborative research, as required in the medical area, where privacy issues preclude data sharing, but where the limitations of local data demand information derived from larger datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00361v1-abstract-full').style.display = 'none'; document.getElementById('2205.00361v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.06224">arXiv:2203.06224</a> <span> [<a href="https://arxiv.org/pdf/2203.06224">pdf</a>, <a href="https://arxiv.org/format/2203.06224">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5753/stil.2021.17803">10.5753/stil.2021.17803 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> verBERT: Automating Brazilian Case Law Document Multi-label Categorization Using BERT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Serras%2C+F+R">Felipe R. Serras</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.06224v1-abstract-short" style="display: inline;"> In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the Kollemata Project to produce two distinct datasets with adequate class systems. Then, we implemented a multi-class and multi-label version of BERT and fine-tuned different BERT models with the produced datasets. We evaluated several… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.06224v1-abstract-full').style.display = 'inline'; document.getElementById('2203.06224v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.06224v1-abstract-full" style="display: none;"> In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the Kollemata Project to produce two distinct datasets with adequate class systems. Then, we implemented a multi-class and multi-label version of BERT and fine-tuned different BERT models with the produced datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as our main metric for which we obtained a performance value of F1-micro=0.72 corresponding to gains of 30 percent points over the tested statistical baseline. In this work, we carried out a study about the use of attention-based algorithms to automate the categorization of Brazilian case law documents. We used data from the \textit{Kollemata} Project to produce two distinct datasets with adequate class systems. Then, we implemented a multi-class and multi-label version of BERT and fine-tuned different BERT models with the produced datasets. We evaluated several metrics, adopting the micro-averaged F1-Score as our main metric for which we obtained a performance value of $\langle \mathcal{F}_1 \rangle_{micro}=0.72$ corresponding to gains of 30 percent points over the tested statistical baseline. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.06224v1-abstract-full').style.display = 'none'; document.getElementById('2203.06224v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> SERRAS, F. R.; FINGER, M. verBERT: Automating Brazilian Case Law Document Multi-label Categorization Using BERT. In 13th Brazilian Simposiun on Human Language and Information Technology (STIL), 2021. pp. 237-246 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.00746">arXiv:2201.00746</a> <span> [<a href="https://arxiv.org/pdf/2201.00746">pdf</a>, <a href="https://arxiv.org/format/2201.00746">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> Coherence of probabilistic constraints on Nash equilibria </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Preto%2C+S">Sandro Preto</a>, <a href="/search/cs?searchtype=author&query=Ferm%C3%A9%2C+E">Eduardo Ferm茅</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.00746v1-abstract-short" style="display: inline;"> Observable games are game situations that reach one of possibly many Nash equilibria. Before an instance of the game starts, an external observer does not know, a priori, what is the exact profile of actions that will occur; thus, he assigns subjective probabilities to players' actions. However, not all probabilistic assignments are coherent with a given game. We study the decision problem of dete… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.00746v1-abstract-full').style.display = 'inline'; document.getElementById('2201.00746v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.00746v1-abstract-full" style="display: none;"> Observable games are game situations that reach one of possibly many Nash equilibria. Before an instance of the game starts, an external observer does not know, a priori, what is the exact profile of actions that will occur; thus, he assigns subjective probabilities to players' actions. However, not all probabilistic assignments are coherent with a given game. We study the decision problem of determining if a given set of probabilistic constraints assigned a priori by the observer to a given game is coherent, which we call the Coherence of Probabilistic Constraints on Equilibria, or PCE-Coherence. We show several results concerning algorithms and complexity for PCE-Coherence when only pure Nash equilibria are considered. In this context, we also study the computation of maximal and minimal probabilistic constraints on actions that preserves coherence. Finally, we study these problems when mixed Nash equilibria are allowed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.00746v1-abstract-full').style.display = 'none'; document.getElementById('2201.00746v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.00904">arXiv:2102.00904</a> <span> [<a href="https://arxiv.org/pdf/2102.00904">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5753/bresci.2021.15797">10.5753/bresci.2021.15797 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Text-to-hashtag Generation using Seq2seq Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Camargo%2C+A">Augusto Camargo</a>, <a href="/search/cs?searchtype=author&query=Carvalho%2C+W">Wesley Carvalho</a>, <a href="/search/cs?searchtype=author&query=Peressim%2C+F">Felipe Peressim</a>, <a href="/search/cs?searchtype=author&query=Barzilay%2C+A">Alan Barzilay</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.00904v2-abstract-short" style="display: inline;"> In this paper, we studied whether models based on BiLSTM and BERT can predict hashtags in Brazilian Portuguese for Ecommerce websites. Hashtags have a sizable financial impact on Ecommerce. We processed a corpus of Ecommerce reviews as inputs, and predicted hashtags as outputs. We evaluated the results using four quantitative metrics: NIST, BLEU, METEOR and a crowdsourced score. A word cloud was u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.00904v2-abstract-full').style.display = 'inline'; document.getElementById('2102.00904v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.00904v2-abstract-full" style="display: none;"> In this paper, we studied whether models based on BiLSTM and BERT can predict hashtags in Brazilian Portuguese for Ecommerce websites. Hashtags have a sizable financial impact on Ecommerce. We processed a corpus of Ecommerce reviews as inputs, and predicted hashtags as outputs. We evaluated the results using four quantitative metrics: NIST, BLEU, METEOR and a crowdsourced score. A word cloud was used as a qualitative metric. While all computer-generated metrics (NIST, BLEU and METEOR) indicated bad results, the crowdsourced results produced amazing scores. We concluded that the texts predicted by the neural networks are very promising for use as hashtags for products on Ecommerce websites. The code for this work is available at https://github.com/augustocamargo/text-to-hashtag. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.00904v2-abstract-full').style.display = 'none'; document.getElementById('2102.00904v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.10405">arXiv:1908.10405</a> <span> [<a href="https://arxiv.org/pdf/1908.10405">pdf</a>, <a href="https://arxiv.org/ps/1908.10405">ps</a>, <a href="https://arxiv.org/format/1908.10405">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-030-22102-7">10.1007/978-3-030-22102-7 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Extending Description Logic EL++ with Linear Constraints on the Probability of Axioms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.10405v1-abstract-short" style="display: inline;"> One of the main reasons to employ a description logic such as EL or EL++ is the fact that it has efficient, polynomial-time algorithmic properties such as deciding consistency and inferring subsumption. However, simply by adding negation of concepts to it, we obtain the expressivity of description logics whose decision procedure is {ExpTime}-complete. Similar complexity explosion occurs if we add… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.10405v1-abstract-full').style.display = 'inline'; document.getElementById('1908.10405v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.10405v1-abstract-full" style="display: none;"> One of the main reasons to employ a description logic such as EL or EL++ is the fact that it has efficient, polynomial-time algorithmic properties such as deciding consistency and inferring subsumption. However, simply by adding negation of concepts to it, we obtain the expressivity of description logics whose decision procedure is {ExpTime}-complete. Similar complexity explosion occurs if we add probability assignments on concepts. To lower the resulting complexity, we instead concentrate on assigning probabilities to Axioms (GCIs). We show that the consistency detection problem for such a probabilistic description logic is NP-complete, and present a linear algebraic deterministic algorithm to solve it, using the column generation technique. We also examine and provide algorithms for the probabilistic extension problem, which consists of inferring the minimum and maximum probabilities for a new axiom, given a consistent probabilistic knowledge base. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.10405v1-abstract-full').style.display = 'none'; document.getElementById('1908.10405v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">An earlier version of this work has appeared at Franz Baader's festschrift. Here we detail the column generation method and present a detailed example</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 03B70; 03B48 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.4; F.4.1; G.3 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In Lecture Notes in Computer Science 11560, pp. 286--300. Springer (2019) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.05704">arXiv:1905.05704</a> <span> [<a href="https://arxiv.org/pdf/1905.05704">pdf</a>, <a href="https://arxiv.org/format/1905.05704">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A logical-based corpus for cross-lingual evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Salvatore%2C+F">Felipe Salvatore</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a>, <a href="/search/cs?searchtype=author&query=Hirata%2C+R">Roberto Hirata Jr</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.05704v5-abstract-short" style="display: inline;"> At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.05704v5-abstract-full').style.display = 'inline'; document.getElementById('1905.05704v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.05704v5-abstract-full" style="display: none;"> At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.05704v5-abstract-full').style.display = 'none'; document.getElementById('1905.05704v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in the proceedings of the Deep Learning for low-resource NLP (DeepLo) workshop at EMNLP 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.05665">arXiv:1905.05665</a> <span> [<a href="https://arxiv.org/pdf/1905.05665">pdf</a>, <a href="https://arxiv.org/ps/1905.05665">ps</a>, <a href="https://arxiv.org/format/1905.05665">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Quantitative Logic Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.05665v1-abstract-short" style="display: inline;"> In this paper we show several similarities among logic systems that deal simultaneously with deductive and quantitative inference. We claim it is appropriate to call the tasks those systems perform as Quantitative Logic Reasoning. Analogous properties hold throughout that class, for whose members there exists a set of linear algebraic techniques applicable in the study of satisfiability decision p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.05665v1-abstract-full').style.display = 'inline'; document.getElementById('1905.05665v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.05665v1-abstract-full" style="display: none;"> In this paper we show several similarities among logic systems that deal simultaneously with deductive and quantitative inference. We claim it is appropriate to call the tasks those systems perform as Quantitative Logic Reasoning. Analogous properties hold throughout that class, for whose members there exists a set of linear algebraic techniques applicable in the study of satisfiability decision problems. In this presentation, we consider as Quantitative Logic Reasoning the tasks performed by propositional Probabilistic Logic; first-order logic with counting quantifiers over a fragment containing unary and limited binary predicates; and propositional Lukasiewicz Infinitely-valued Probabilistic Logic <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.05665v1-abstract-full').style.display = 'none'; document.getElementById('1905.05665v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Appeared as a chapter in Trends in Logic series</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In W. Carnielli and J. Malinowski, editors, Contradictions, from Consistency to Inconsistency, Trends in Logic, pages 241-272. Springer International Publishing, 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.07108">arXiv:1807.07108</a> <span> [<a href="https://arxiv.org/pdf/1807.07108">pdf</a>, <a href="https://arxiv.org/format/1807.07108">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luz%2C+F+F">Fabiano Ferreira Luz</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1807.07108v1-abstract-short" style="display: inline;"> Semantic parsing can be defined as the process of mapping natural language sentences into a machine interpretable, formal representation of its meaning. Semantic parsing using LSTM encoder-decoder neural networks have become promising approach. However, human automated translation of natural language does not provide grammaticality guarantees for the sentences generate such a guarantee is particul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07108v1-abstract-full').style.display = 'inline'; document.getElementById('1807.07108v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.07108v1-abstract-full" style="display: none;"> Semantic parsing can be defined as the process of mapping natural language sentences into a machine interpretable, formal representation of its meaning. Semantic parsing using LSTM encoder-decoder neural networks have become promising approach. However, human automated translation of natural language does not provide grammaticality guarantees for the sentences generate such a guarantee is particularly important for practical cases where a data base query can cause critical errors if the sentence is ungrammatical. In this work, we propose an neural architecture called Encoder CFG-Decoder, whose output conforms to a given context-free grammar. Results are show for any implementation of such architecture display its correctness and providing benchmark accuracy levels better than the literature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.07108v1-abstract-full').style.display = 'none'; document.getElementById('1807.07108v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.04329">arXiv:1803.04329</a> <span> [<a href="https://arxiv.org/pdf/1803.04329">pdf</a>, <a href="https://arxiv.org/format/1803.04329">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Luz%2C+F+F">Fabiano Ferreira Luz</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.04329v1-abstract-short" style="display: inline;"> Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.04329v1-abstract-full').style.display = 'inline'; document.getElementById('1803.04329v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.04329v1-abstract-full" style="display: none;"> Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our approach is based on vector space model and neural networks. The proposed model is based in two learning steps. The first step generates a vector representation for the sentence in natural language and SPARQL query. The second step uses this vector representation as input to a neural network (LSTM with attention mechanism) to generate a model able to encode natural language and decode SPARQL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.04329v1-abstract-full').style.display = 'none'; document.getElementById('1803.04329v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1203.6161">arXiv:1203.6161</a> <span> [<a href="https://arxiv.org/pdf/1203.6161">pdf</a>, <a href="https://arxiv.org/ps/1203.6161">ps</a>, <a href="https://arxiv.org/format/1203.6161">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.4204/EPTCS.81.6">10.4204/EPTCS.81.6 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Classical and quantum satisfiability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=de+Ara%C3%BAjo%2C+A">Anderson de Ara煤jo</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1203.6161v1-abstract-short" style="display: inline;"> We present the linear algebraic definition of QSAT and propose a direct logical characterization of such a definition. We then prove that this logical version of QSAT is not an extension of classical satisfiability problem (SAT). This shows that QSAT does not allow a direct comparison between the complexity classes NP and QMA, for which SAT and QSAT are respectively complete. </span> <span class="abstract-full has-text-grey-dark mathjax" id="1203.6161v1-abstract-full" style="display: none;"> We present the linear algebraic definition of QSAT and propose a direct logical characterization of such a definition. We then prove that this logical version of QSAT is not an extension of classical satisfiability problem (SAT). This shows that QSAT does not allow a direct comparison between the complexity classes NP and QMA, for which SAT and QSAT are respectively complete. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1203.6161v1-abstract-full').style.display = 'none'; document.getElementById('1203.6161v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In Proceedings LSFA 2011, arXiv:1203.5423</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> EPTCS 81, 2012, pp. 79-84 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1202.4144">arXiv:1202.4144</a> <span> [<a href="https://arxiv.org/pdf/1202.4144">pdf</a>, <a href="https://arxiv.org/ps/1202.4144">ps</a>, <a href="https://arxiv.org/format/1202.4144">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.entcs.2009.11.007">10.1016/j.entcs.2009.11.007 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Towards an efficient prover for the C1 paraconsistent logic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neto%2C+A">Adolfo Neto</a>, <a href="/search/cs?searchtype=author&query=Kaestner%2C+C+A+A">Celso A. A. Kaestner</a>, <a href="/search/cs?searchtype=author&query=Finger%2C+M">Marcelo Finger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1202.4144v1-abstract-short" style="display: inline;"> The KE inference system is a tableau method developed by Marco Mondadori which was presented as an improvement, in the computational efficiency sense, over Analytic Tableaux. In the literature, there is no description of a theorem prover based on the KE method for the C1 paraconsistent logic. Paraconsistent logics have several applications, such as in robot control and medicine. These applications… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1202.4144v1-abstract-full').style.display = 'inline'; document.getElementById('1202.4144v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1202.4144v1-abstract-full" style="display: none;"> The KE inference system is a tableau method developed by Marco Mondadori which was presented as an improvement, in the computational efficiency sense, over Analytic Tableaux. In the literature, there is no description of a theorem prover based on the KE method for the C1 paraconsistent logic. Paraconsistent logics have several applications, such as in robot control and medicine. These applications could benefit from the existence of such a prover. We present a sound and complete KE system for C1, an informal specification of a strategy for the C1 prover as well as problem families that can be used to evaluate provers for C1. The C1 KE system and the strategy described in this paper will be used to implement a KE based prover for C1, which will be useful for those who study and apply paraconsistent logics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1202.4144v1-abstract-full').style.display = 'none'; document.getElementById('1202.4144v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 February, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2012. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Electronic Notes in Theoretical Computer Science. Volume 256, 2 December 2009, Pages 87-102. Proceedings of the Fourth Workshop on Logical and Semantic Frameworks, with Applications (LSFA 2009) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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