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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Pham, T D"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" 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/2501.07102">arXiv:2501.07102</a> <span> [<a href="https://arxiv.org/pdf/2501.07102">pdf</a>, <a href="https://arxiv.org/format/2501.07102">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="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> <p class="title is-5 mathjax"> AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chu%2C+T+C">The Chuong Chu</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+V+T+D">Vu Tuan Dat Pham</a>, <a href="/search/cs?searchtype=author&query=Dao%2C+K">Kien Dao</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+H">Hoang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Truong%2C+Q+H">Quoc Hung Truong</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="2501.07102v1-abstract-short" style="display: inline;"> Intra-sentential code-switching (CS) refers to the alternation between languages that happens within a single utterance and is a significant challenge for Automatic Speech Recognition (ASR) systems. For example, when a Vietnamese speaker uses foreign proper names or specialized terms within their speech. ASR systems often struggle to accurately transcribe intra-sentential CS due to their training… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07102v1-abstract-full').style.display = 'inline'; document.getElementById('2501.07102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.07102v1-abstract-full" style="display: none;"> Intra-sentential code-switching (CS) refers to the alternation between languages that happens within a single utterance and is a significant challenge for Automatic Speech Recognition (ASR) systems. For example, when a Vietnamese speaker uses foreign proper names or specialized terms within their speech. ASR systems often struggle to accurately transcribe intra-sentential CS due to their training on monolingual data and the unpredictable nature of CS. This issue is even more pronounced for low-resource languages, where limited data availability hinders the development of robust models. In this study, we propose AdaCS, a normalization model integrates an adaptive bias attention module (BAM) into encoder-decoder network. This novel approach provides a robust solution to CS ASR in unseen domains, thereby significantly enhancing our contribution to the field. By utilizing BAM to both identify and normalize CS phrases, AdaCS enhances its adaptive capabilities with a biased list of words provided during inference. Our method demonstrates impressive performance and the ability to handle unseen CS phrases across various domains. Experiments show that AdaCS outperforms previous state-of-the-art method on Vietnamese CS ASR normalization by considerable WER reduction of 56.2% and 36.8% on the two proposed test sets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07102v1-abstract-full').style.display = 'none'; document.getElementById('2501.07102v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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">Accepted at ICASSP 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03292">arXiv:2410.03292</a> <span> [<a href="https://arxiv.org/pdf/2410.03292">pdf</a>, <a href="https://arxiv.org/format/2410.03292">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> <p class="title is-5 mathjax"> Demystifying the Token Dynamics of Deep Selective State Space Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vo%2C+T+N">Thieu N Vo</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Tung D. Pham</a>, <a href="/search/cs?searchtype=author&query=Tong%2C+X+T">Xin T. Tong</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T+M">Tan Minh Nguyen</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="2410.03292v1-abstract-short" style="display: inline;"> Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03292v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03292v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03292v1-abstract-full" style="display: none;"> Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properties of tokens in a pre-trained Mamba model. In particular, we derive the dynamical system governing the continuous-time limit of the Mamba model and characterize the asymptotic behavior of its solutions. In the one-dimensional case, we prove that only one of the following two scenarios happens: either all tokens converge to zero, or all tokens diverge to infinity. We provide criteria based on model parameters to determine when each scenario occurs. For the convergent scenario, we empirically verify that this scenario negatively impacts the model's performance. For the divergent scenario, we prove that different tokens will diverge to infinity at different rates, thereby contributing unequally to the updates during model training. Based on these investigations, we propose two refinements for the model: excluding the convergent scenario and reordering tokens based on their importance scores, both aimed at improving practical performance. Our experimental results validate these refinements, offering insights into enhancing Mamba's effectiveness in real-world applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03292v1-abstract-full').style.display = 'none'; document.getElementById('2410.03292v1-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> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.12480">arXiv:2408.12480</a> <span> [<a href="https://arxiv.org/pdf/2408.12480">pdf</a>, <a href="https://arxiv.org/format/2408.12480">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Doan%2C+K+T">Khang T. Doan</a>, <a href="/search/cs?searchtype=author&query=Huynh%2C+B+G">Bao G. Huynh</a>, <a href="/search/cs?searchtype=author&query=Hoang%2C+D+T">Dung T. Hoang</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Thuc D. Pham</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+N+H">Nhat H. Pham</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+Q+T+M">Quan T. M. Nguyen</a>, <a href="/search/cs?searchtype=author&query=Vo%2C+B+Q">Bang Q. Vo</a>, <a href="/search/cs?searchtype=author&query=Hoang%2C+S+N">Suong N. 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="2408.12480v2-abstract-short" style="display: inline;"> In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. By integrating the Qwen2-0.5B-Instruct language model with the InternViT-300M-448px visual model, Vintern-1B is optimized for a range of applications, including optical character recognition (OCR), document extraction, and general question-answering in Viet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12480v2-abstract-full').style.display = 'inline'; document.getElementById('2408.12480v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.12480v2-abstract-full" style="display: none;"> In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. By integrating the Qwen2-0.5B-Instruct language model with the InternViT-300M-448px visual model, Vintern-1B is optimized for a range of applications, including optical character recognition (OCR), document extraction, and general question-answering in Vietnamese context. The model is fine-tuned on an extensive dataset of over 3 million image-question-answer pairs, achieving robust performance and reliable results across multiple Vietnamese language benchmarks like OpenViVQA and ViTextVQA. Vintern-1B is small enough to fit into various on-device applications easily. Additionally, we have open-sourced several Vietnamese vision question answering (VQA) datasets for text and diagrams, created with Gemini 1.5 Flash. Our models are available at: https://huggingface.co/5CD-AI/Vintern-1B-v2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12480v2-abstract-full').style.display = 'none'; document.getElementById('2408.12480v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.06436">arXiv:2401.06436</a> <span> [<a href="https://arxiv.org/pdf/2401.06436">pdf</a>, <a href="https://arxiv.org/format/2401.06436">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="Information Retrieval">cs.IR</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/10.1109/KSE53942.2021.9648823">10.1109/KSE53942.2021.9648823 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hoang%2C+T+L">Thi Linh Hoang</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Tuan Dung Pham</a>, <a href="/search/cs?searchtype=author&query=Ta%2C+V+C">Viet Cuong Ta</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="2401.06436v1-abstract-short" style="display: inline;"> In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder architecture for node embedding in the network. Using the embedding layer from the graph-based convolution layer, the attention mechanism could rearran… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06436v1-abstract-full').style.display = 'inline'; document.getElementById('2401.06436v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.06436v1-abstract-full" style="display: none;"> In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder architecture for node embedding in the network. Using the embedding layer from the graph-based convolution layer, the attention mechanism could rearrange the feature space to get a more efficient embedding for the downstream task. The experiments showed that our proposed architecture achieves better performance than GCN on the traditional link prediction task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06436v1-abstract-full').style.display = 'none'; document.getElementById('2401.06436v1-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.09184">arXiv:2302.09184</a> <span> [<a href="https://arxiv.org/pdf/2302.09184">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</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/10.1038/s41524-023-01125-1">10.1038/s41524-023-01125-1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Comlek%2C+Y">Yigitcan Comlek</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Thang Duc Pham</a>, <a href="/search/cs?searchtype=author&query=Snurr%2C+R">Randall Snurr</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wei Chen</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="2302.09184v1-abstract-short" style="display: inline;"> Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of pote… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09184v1-abstract-full').style.display = 'inline'; document.getElementById('2302.09184v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.09184v1-abstract-full" style="display: none;"> Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of potential building blocks leads to a combinatorial challenge, with millions of possible MOFs that could be explored through time consuming physics-based approaches. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently without any human intervention. Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification. To demonstrate the effectiveness of our method, we considered a design space with more than 47,000 MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able to identify all MOFs on the Pareto front and more than 97% of the 50 top-performing designs for the CO$_2$ working capacity and CO$_2$/N$_2$ selectivity properties. Finally, we compared our approach with the Random Forest algorithm and demonstrated its efficiency, interpretability, and robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09184v1-abstract-full').style.display = 'none'; document.getElementById('2302.09184v1-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">35 pages total. First 29 pages belong to the main manuscript and the remaining 6 six are for the supplementary information, 13 figures total. 9 figures are on the main manuscript and 4 figures are in the supplementary information. 1 table in the supplementary information</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.14493">arXiv:2211.14493</a> <span> [<a href="https://arxiv.org/pdf/2211.14493">pdf</a>, <a href="https://arxiv.org/format/2211.14493">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> </div> </div> <p class="title is-5 mathjax"> Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+Y">Yuan Sun</a>, <a href="/search/cs?searchtype=author&query=Nathan-Roberts%2C+W">Winton Nathan-Roberts</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Tien Dung Pham</a>, <a href="/search/cs?searchtype=author&query=Otte%2C+E">Ellen Otte</a>, <a href="/search/cs?searchtype=author&query=Aickelin%2C+U">Uwe Aickelin</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="2211.14493v1-abstract-short" style="display: inline;"> In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a statistical machine learning approach,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14493v1-abstract-full').style.display = 'inline'; document.getElementById('2211.14493v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.14493v1-abstract-full" style="display: none;"> In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a statistical machine learning approach, multi-fidelity Gaussian process, for process modelling in biomanufacturing. Gaussian process regression is a well-established technique based on probability theory which can naturally consider uncertainty in a dataset via Gaussian noise, and multi-fidelity techniques can make use of multiple sources of information with different levels of fidelity, thus suitable for bioprocess modeling with small data. We apply the multi-fidelity Gaussian process to solve two significant problems in biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines, and demonstrate its efficacy on real-world datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.14493v1-abstract-full').style.display = 'none'; document.getElementById('2211.14493v1-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.04520">arXiv:2206.04520</a> <span> [<a href="https://arxiv.org/pdf/2206.04520">pdf</a>, <a href="https://arxiv.org/format/2206.04520">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</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 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/10.1007/978-3-031-15063-0_26">10.1007/978-3-031-15063-0_26 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An FPGA-based Solution for Convolution Operation Acceleration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Trung Dinh Pham</a>, <a href="/search/cs?searchtype=author&query=Bach%2C+B+G">Bao Gia Bach</a>, <a href="/search/cs?searchtype=author&query=Luu%2C+L+T">Lam Trinh Luu</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+M+D">Minh Dinh Nguyen</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+H+D">Hai Duc Pham</a>, <a href="/search/cs?searchtype=author&query=Anh%2C+K+B">Khoa Bui Anh</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+X+Q">Xuan Quang Nguyen</a>, <a href="/search/cs?searchtype=author&query=Quoc%2C+C+P">Cuong Pham Quoc</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="2206.04520v1-abstract-short" style="display: inline;"> Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step that appears in many Convolutional Neural Network models. We target the design to the standard convolution operation, intending to launch the product a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04520v1-abstract-full').style.display = 'inline'; document.getElementById('2206.04520v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.04520v1-abstract-full" style="display: none;"> Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step that appears in many Convolutional Neural Network models. We target the design to the standard convolution operation, intending to launch the product as an edge-AI solution. The project's purpose is to produce an FPGA IP core that can process a convolutional layer at a time. System developers can deploy the IP core with various FPGA families by using Verilog HDL as the primary design language for the architecture. The experimental results show that our single computing core synthesized on a simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully utilized, 4.48 GOPS can be achieved. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04520v1-abstract-full').style.display = 'none'; document.getElementById('2206.04520v1-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> 9 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">11 pages, 6 figures, accepted to The First International Conference on Intelligence of Things (ICIT 2022)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, 2022, </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.02504">arXiv:2110.02504</a> <span> [<a href="https://arxiv.org/pdf/2110.02504">pdf</a>, <a href="https://arxiv.org/format/2110.02504">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> </div> </div> <p class="title is-5 mathjax"> Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chaganti%2C+R">Rajasekhar Chaganti</a>, <a href="/search/cs?searchtype=author&query=Ravi%2C+V">Vinayakumar Ravi</a>, <a href="/search/cs?searchtype=author&query=Alazab%2C+M">Mamoun Alazab</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Tuan D. Pham</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="2110.02504v1-abstract-short" style="display: inline;"> Malware distribution to the victim network is commonly performed through file attachments in phishing email or from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage techniques such as signature or anomaly-based, machine learning techniques. The well-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.02504v1-abstract-full').style.display = 'inline'; document.getElementById('2110.02504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.02504v1-abstract-full" style="display: none;"> Malware distribution to the victim network is commonly performed through file attachments in phishing email or from the internet, when the victim interacts with the source of infection. To detect and prevent the malware distribution in the victim machine, the existing end device security applications may leverage techniques such as signature or anomaly-based, machine learning techniques. The well-known file formats Portable Executable (PE) for Windows and Executable and Linkable Format (ELF) for Linux based operating system are used for malware analysis, and the malware detection capabilities of these files has been well advanced for real-time detection. But the malware payload hiding in multimedia using steganography detection has been a challenge for enterprises, as these are rarely seen and usually act as a stager in sophisticated attacks. In this article, to our knowledge, we are the first to try to address the knowledge gap between the current progress in image steganography and steganalysis academic research focusing on data hiding and the review of the stegomalware (malware payload hiding in images) targeting enterprises with cyberattacks current status. We present the stegomalware history, generation tools, file format specification description. Based on our findings, we perform the detail review of the image steganography techniques including the recent Generative Adversarial Networks (GAN) based models and the image steganalysis methods including the Deep Learning(DL) models for hiding data detection. Additionally, the stegomalware detection framework for enterprise is proposed for anomaly based stegomalware detection emphasizing the architecture details for different network environments. Finally, the research opportunities and challenges in stegomalware generation and detection are also presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.02504v1-abstract-full').style.display = 'none'; document.getElementById('2110.02504v1-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> 6 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.01206">arXiv:1811.01206</a> <span> [<a href="https://arxiv.org/pdf/1811.01206">pdf</a>, <a href="https://arxiv.org/format/1811.01206">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 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.knosys.2019.04.025">10.1016/j.knosys.2019.04.025 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DUNet: A deformable network for retinal vessel segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jin%2C+Q">Qiangguo Jin</a>, <a href="/search/cs?searchtype=author&query=Meng%2C+Z">Zhaopeng Meng</a>, <a href="/search/cs?searchtype=author&query=Pham%2C+T+D">Tuan D. Pham</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Q">Qi Chen</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+L">Leyi Wei</a>, <a href="/search/cs?searchtype=author&query=Su%2C+R">Ran Su</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="1811.01206v1-abstract-short" style="display: inline;"> Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutiona… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.01206v1-abstract-full').style.display = 'inline'; document.getElementById('1811.01206v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.01206v1-abstract-full" style="display: none;"> Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level feature maps with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels' scales and shapes. Three public datasets DRIVE, STARE and CHASE_DB1 are used to train and test our model. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9697/0.9722/0.9724 and AUC of 0.9856/0.9868/0.9863 on DRIVE, STARE and CHASE_DB1 respectively. Moreover, to show the generalization ability of the DUNet, we used another two retinal vessel data sets, one is named WIDE and the other is a synthetic data set with diverse styles, named SYNTHE, to qualitatively and quantitatively analyzed and compared with other methods. Results indicates that DUNet outperforms other state-of-the-arts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.01206v1-abstract-full').style.display = 'none'; document.getElementById('1811.01206v1-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 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </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 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