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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/2407.13803">arXiv:2407.13803</a> <span> [<a href="https://arxiv.org/pdf/2407.13803">pdf</a>, <a href="https://arxiv.org/format/2407.13803">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hoang%2C+D+C">Duy C. Hoang</a>, <a href="/search/cs?searchtype=author&query=Le%2C+H+T+Q">Hung T. Q. Le</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+R">Rui Chu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+P">Ping Li</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+W">Weijie Zhao</a>, <a href="/search/cs?searchtype=author&query=Lao%2C+Y">Yingjie Lao</a>, <a href="/search/cs?searchtype=author&query=Doan%2C+K+D">Khoa D. Doan</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="2407.13803v1-abstract-short" style="display: inline;"> With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13803v1-abstract-full').style.display = 'inline'; document.getElementById('2407.13803v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13803v1-abstract-full" style="display: none;"> With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. In this work, we present a novel type of LLM watermark, Sparse Watermark, which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. The key strategy involves anchoring watermarked tokens to words that have specific Part-of-Speech (POS) tags. Our experimental results demonstrate that the proposed watermarking scheme achieves high detectability while generating text that outperforms previous LLM watermarking methods in quality across various tasks <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13803v1-abstract-full').style.display = 'none'; document.getElementById('2407.13803v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.01987">arXiv:2407.01987</a> <span> [<a href="https://arxiv.org/pdf/2407.01987">pdf</a>, <a href="https://arxiv.org/format/2407.01987">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> AHMsys: An Automated HVAC Modeling System for BIM Project </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dang%2C+L+H">Long Hoang Dang</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Duy-Hung Nguyen</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Thai Quang Le</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T+T">Thinh Truong Nguyen</a>, <a href="/search/cs?searchtype=author&query=Mei%2C+C">Clark Mei</a>, <a href="/search/cs?searchtype=author&query=Hoang%2C+V">Vu Hoang</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="2407.01987v1-abstract-short" style="display: inline;"> This paper presents a novel system, named AHMsys, designed to automate the process of generating 3D Heating, Ventilation, and Air Conditioning (HVAC) models from 2D Computer-Aided Design (CAD) drawings, a key component of Building Information Modeling (BIM). By automatically preprocessing and extracting essential HVAC object information then creating detailed 3D models, our proposed AHMsys signifi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01987v1-abstract-full').style.display = 'inline'; document.getElementById('2407.01987v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.01987v1-abstract-full" style="display: none;"> This paper presents a novel system, named AHMsys, designed to automate the process of generating 3D Heating, Ventilation, and Air Conditioning (HVAC) models from 2D Computer-Aided Design (CAD) drawings, a key component of Building Information Modeling (BIM). By automatically preprocessing and extracting essential HVAC object information then creating detailed 3D models, our proposed AHMsys significantly reduced the 20 percent work schedule of the BIM process in Akila. This advancement highlights the essential impact of integrating AI technologies in managing the lifecycle of a digital representation of the building. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01987v1-abstract-full').style.display = 'none'; document.getElementById('2407.01987v1-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> 2 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18602">arXiv:2406.18602</a> <span> [<a href="https://arxiv.org/pdf/2406.18602">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</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="Computation">stat.CO</span> </div> </div> <p class="title is-5 mathjax"> Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Duy Nguyen</a>, <a href="/search/cs?searchtype=author&query=Hoang%2C+C">Ca Hoang</a>, <a href="/search/cs?searchtype=author&query=Huynh%2C+P+K">Phat K. Huynh</a>, <a href="/search/cs?searchtype=author&query=Truong%2C+T">Tien Truong</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A">Abhay Sharma</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Trung Q. Le</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="2406.18602v1-abstract-short" style="display: inline;"> Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically lack the necessary dynamic and longitudinal scope to accurately forecast CVD trajectories in OSA patients. This study introduces a novel multi-level phenotypic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18602v1-abstract-full').style.display = 'inline'; document.getElementById('2406.18602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18602v1-abstract-full" style="display: none;"> Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically lack the necessary dynamic and longitudinal scope to accurately forecast CVD trajectories in OSA patients. This study introduces a novel multi-level phenotypic model to analyze the progression and interplay of these conditions over time, utilizing data from the Wisconsin Sleep Cohort, which includes 1,123 participants followed for decades. Our methodology comprises three advanced steps: (1) Conducting feature importance analysis through tree-based models to underscore critical predictive variables like total cholesterol, low-density lipoprotein (LDL), and diabetes. (2) Developing a logistic mixed-effects model (LGMM) to track longitudinal transitions and pinpoint significant factors, which displayed a diagnostic accuracy of 0.9556. (3) Implementing t-distributed Stochastic Neighbor Embedding (t-SNE) alongside Gaussian Mixture Models (GMM) to segment patient data into distinct phenotypic clusters that reflect varied risk profiles and disease progression pathways. This phenotypic clustering revealed two main groups, with one showing a markedly increased risk of major adverse cardiovascular events (MACEs), underscored by the significant predictive role of nocturnal hypoxia and sympathetic nervous system activity from sleep data. Analysis of transitions and trajectories with t-SNE and GMM highlighted different progression rates within the cohort, with one cluster progressing more slowly towards severe CVD states than the other. This study offers a comprehensive understanding of the dynamic relationship between CVD and OSA, providing valuable tools for predicting disease onset and tailoring treatment approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18602v1-abstract-full').style.display = 'none'; document.getElementById('2406.18602v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">30 pages, 5 figure, 5 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/2403.01417">arXiv:2403.01417</a> <span> [<a href="https://arxiv.org/pdf/2403.01417">pdf</a>, <a href="https://arxiv.org/format/2403.01417">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cao%2C+T">Tien-Dung Cao</a>, <a href="/search/cs?searchtype=author&query=Vuong%2C+N+T">Nguyen T. Vuong</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Thai Q. Le</a>, <a href="/search/cs?searchtype=author&query=Dao%2C+H+V+N">Hoang V. N. Dao</a>, <a href="/search/cs?searchtype=author&query=Truong-Huu%2C+T">Tram Truong-Huu</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="2403.01417v1-abstract-short" style="display: inline;"> In federated learning, the models can be trained synchronously or asynchronously. Many research works have focused on developing an aggregation method for the server to aggregate multiple local models into the global model with improved performance. They ignore the heterogeneity of the training workers, which causes the delay in the training of the local models, leading to the obsolete information… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01417v1-abstract-full').style.display = 'inline'; document.getElementById('2403.01417v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.01417v1-abstract-full" style="display: none;"> In federated learning, the models can be trained synchronously or asynchronously. Many research works have focused on developing an aggregation method for the server to aggregate multiple local models into the global model with improved performance. They ignore the heterogeneity of the training workers, which causes the delay in the training of the local models, leading to the obsolete information issue. In this paper, we design and develop Asyn2F, an Asynchronous Federated learning Framework with bidirectional model aggregation. By bidirectional model aggregation, Asyn2F, on one hand, allows the server to asynchronously aggregate multiple local models and results in a new global model. On the other hand, it allows the training workers to aggregate the new version of the global model into the local model, which is being trained even in the middle of a training epoch. We develop Asyn2F considering the practical implementation requirements such as using cloud services for model storage and message queuing protocols for communications. Extensive experiments with different datasets show that the models trained by Asyn2F achieve higher performance compared to the state-of-the-art techniques. The experiments also demonstrate the effectiveness, practicality, and scalability of Asyn2F, making it ready for deployment in real scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01417v1-abstract-full').style.display = 'none'; document.getElementById('2403.01417v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00737">arXiv:2311.00737</a> <span> [<a href="https://arxiv.org/pdf/2311.00737">pdf</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="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Huynh%2C+P+K">Phat K. Huynh</a>, <a href="/search/cs?searchtype=author&query=Bui%2C+V+D+A">Vinh Duc An Bui</a>, <a href="/search/cs?searchtype=author&query=Hwang%2C+K+Y">Kee Young Hwang</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+N">Nityanand Jain</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+C">Chau Nguyen</a>, <a href="/search/cs?searchtype=author&query=Minh%2C+L+H+N">Le Huu Nhat Minh</a>, <a href="/search/cs?searchtype=author&query=Van+Truong%2C+L">Le Van Truong</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+X+T">Xuan Thanh Nguyen</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D+H">Dinh Hoang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Dung%2C+L+T">Le Tien Dung</a>, <a href="/search/cs?searchtype=author&query=Le%2C+T+Q">Trung Q. Le</a>, <a href="/search/cs?searchtype=author&query=Phan%2C+M">Manh-Huong Phan</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="2311.00737v1-abstract-short" style="display: inline;"> The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through thre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00737v1-abstract-full').style.display = 'inline'; document.getElementById('2311.00737v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00737v1-abstract-full" style="display: none;"> The COVID-19 pandemic underscored the importance of reliable, noninvasive diagnostic tools for robust public health interventions. In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases. The MRST precisely captures breathing patterns through three specific breath testing protocols: normal breath, holding breath, and deep breath. We collected breath data from both COVID-19 patients and healthy subjects in Vietnam using this platform, which then served to train and validate ML models. Our evaluation encompassed multiple ML algorithms, including support vector machines and deep learning models, assessing their ability to diagnose COVID-19. Our multi-model validation methodology ensures a thorough comparison and grants the adaptability to select the most optimal model, striking a balance between diagnostic precision with model interpretability. The findings highlight the exceptional potential of our diagnostic tool in pinpointing respiratory anomalies, achieving over 90% accuracy. This innovative sensor technology can be seamlessly integrated into healthcare settings for patient monitoring, marking a significant enhancement for the healthcare infrastructure. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00737v1-abstract-full').style.display = 'none'; document.getElementById('2311.00737v1-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> 1 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </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 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