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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Huang, Tung-Sheng Yu, Ping-Chung Su, Li Academia Sinica Institute of Information Science Taiwan Georgia Institute of Technology College of Computing GA United States National Tsing Hua University Department of Electrical Engineering Taiwan

ISBN: (纸本)9781728163277

Transcribing electric guitar solo in real-world performance is challenging because of the interference of background accompaniments, the strong coupling between music pitch and playing technique, and the limited resource of data annotation. To address these issues, we first propose a new guitar solo dataset for this task. Then, we propose a transcription model which learns an output space jointly constructed with notes, playing techniques, and two sets of meta-class labels named note states and technique groups, such that the model can harness the layered relationship among different note-level event and playing technique classes. The proposed model outperforms the state-of-the-art guitar solo transcription and note transcription models. © 2023 IEEE.

关键词： attention Automatic music transcription deep learning electric guitar U-net

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Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Development of Interpretable Machine Learning Models for COV...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shi, Wenqi Murakoso, Mio Guo, Xiaoyan Wang, May D. Georgia Institute of Technology Department of Electrical and Computer Engineering Atlanta United States Kyoto University Department of Applied Life Science Kyoto Japan Georgia Institute of Technology College of Computing Atlanta United States Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798350337488

With the extensive time and financial requirements incumbent on drug discovery, computational approaches, such as protein-ligand docking predictions, are increasingly crucial for accelerating the process of drug repurposing. However, the proliferation of identified protein targets has exposed a critical knowledge gap in developing robust models that offer both generalizability and interpretability for docking score prediction. Addressing this, our study presents a machine learning-based surrogate model, employing interpretable artificial intelligence techniques for accurate docking score prediction for SARS-CoV-2 protein targets. We demonstrate the model generalization on its expansion to accommodate unseen protein targets by integrating protein target information through feature concatenation. Moreover, we leverage the SHapley Additive exPlanations (SHAP) method to identify the data-driven feature importance of molecular substructures for knowledge-based validation. Our experiments reveal that the combination of data-driven prediction and knowledge-driven validation could provide biomedical insights into the interactions between drugs and SARS-CoV-2 proteins, elucidating their consequent effects on docking scores. © 2023 IEEE.

关键词： COVID-19 drug target explainable artificial intelligence interaction prediction machine learning

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Enhancing the fidelity of social media image data sets in earthquake damage assessment

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Earthquake Spectra 2025年

作者： Huang, Huili Zhang, Danrong Masalava, Anastasiya Roozbahani, Max Mahdi Roy, Nimisha Frost, J David School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States School of Computer Science Georgia Institute of Technology AtlantaGA United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Civil and Environmental Engineering Georgia Institute of Technology AtlantaGA United States

The development of artificial intelligence (AI) provides an opportunity for rapid and accurate assessment of earthquake-induced infrastructure damage using social media images. Nevertheless, data collection and labeling remain challenging due to limited expertise among annotators. This study introduces a novel four-class Earthquake Infrastructure Damage (EID) assessment data set compiled from a combination of images from several other social media image databases but with added emphasis on data quality. Unlike the previous data sets such as Damage Assessment Dataset (DAD) and Crisis Benchmark, the EID includes comprehensive labeling guidelines and a multiclass classification system aligned with established damage scales, such as HAZUS and EMS-98, to enhance the accuracy and utility of social media imagery for disaster response. By integrating detailed descriptions and clear labeling criteria, the labeling approach of EID reduces the subjective nature of image labeling and the inconsistencies found in existing data sets. The findings demonstrate a significant improvement in annotator agreement, reducing disagreement from 39.7% to 10.4%, thereby validating the efficacy of the refined labeling strategy. The EID, containing 13,513 high-quality images from five significant earthquakes, is designed to support community-level assessments and advanced computational research, paving the way for enhanced disaster response strategies through improved data utilization and analysis. The data set is available at DesignSafe: https://***/10.17603/ds2-yj8p-hs62. © The Author(s) 2025.

关键词： Tweets

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Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

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npj computational Materials 2022年第1期8卷 1886-1898页

作者： Zhenze Yang Markus J.Buehler Laboratory for Atomistic and Molecular Mechanics(LAMM) Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA02139USA Department of Materials Science and Engineering Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA02139USA Center for Computational Science and Engineering Schwarzman College of ComputingMassachusetts Institute of Technology77 Massachusetts AveCambridgeMA02139USA Center for Materials Science and Engineering 77 Massachusetts Ave CambridgeMA02139USA

Structural defects are abundant in solids,and vital to the macroscopic materials ***,a defect-property linkage typically requires significant efforts from experiments or simulations,and often contains limited information due to the breadth of nanoscopic design *** we report a graph neural network(GNN)-based approach to achieve direct translation between mesoscale crystalline structures and atom-level properties,emphasizing the effects of structural *** end-to-end method offers great performance and generality in predicting both atomic stress and potential energy of multiple systems with different ***,the approach also precisely captures derivative properties which strictly observe physical laws and reproduces evolution of properties with varying boundary *** incorporating a genetic algorithm,we then design de novo atomic structures with optimum global properties and target local *** method would significantly enhance the efficiency of evaluating atomic behaviors given structural imperfections and accelerates the design process at the meso-level.

关键词： structural solids crystalline

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Schema-Driven Information Extraction from Heterogeneous Tables

Schema-Driven Information Extraction from Heterogeneous Tabl...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Bai, Fan Kang, Junmo Stanovsky, Gabriel Freitag, Dayne Dredze, Mark Ritter, Alan College of Computing Georgia Institute of Technology United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Artificial Intelligence Center SRI International Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761681

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from *** introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored *** assess various LLM's capabilities on this task, we present a benchmark comprised of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and *** use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data *** experiments demonstrate that surprisingly competitive performance can be achieved without requiring task-specific pipelines or labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining cost ***, through detailed ablation studies and analyses, we investigate the factors contributing to model success and validate the practicality of distilling compact models to reduce API reliance. © 2024 Association for computational Linguistics.

关键词： Data mining

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Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets

Effective Surrogate Models for Docking Scores Prediction of ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shi, Wenqi Murakoso, Mio Guo, Xiaoyan Xiong, Linxi Chen, Matthew Wang, May D. Georgia Institute of Technology Department of Electrical and Computer Engineering Atlanta United States Kyoto University Department of Applied Life Science Kyoto Japan Georgia Institute of Technology College of Computing Atlanta United States Tsinghua University Tanwei College Beijing China Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798350337488

Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major threat to public health and present a critical challenge for drug discovery. Due to the cost- and time-consuming process of new drug development, virtual pre-screening methods such as protein-ligand docking prediction have become essential tools in enhancing drug refurbishment and repurposing. In this study, we propose a machine learning-based surrogate model for docking score prediction of drug candidates on SARS-CoV-2 protein targets via deep feature concatenation. We investigate 14 different combinations of rule-based and data-driven fingerprinting methods to identify the optimal representation of candidate drug molecules. Extensive experiments on docking scores of 270,000 molecules across 18 different SARS-CoV-2 protein targets demonstrate the effectiveness of the proposed surrogate models. In addition to unseen drugs, we further investigate the generalization of the proposed framework for unseen protein targets. This study may provide an instrumental and generalizable framework for exploring ligand-protein interaction, serving as a useful tool to facilitate rapid drug pre-screening during emerging public health crises. © 2023 IEEE.

关键词： COVID-19 drug target explainable artificial intelligence feature concatenation interaction prediction neural networks

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Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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