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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Deng, Jamie Wu, Yusen Hayssen, Hilary Englum, Brain Kankaria, Aman Mayorga-Carlin, Minerva Sahoo, Shalini Sorkin, John Lal, Brajesh Yesha, Yelena Nguyen, Phuong University of Miami Frost Institute for Data Science & Computing FL United States University of Maryland School of Medicine MD United States

ISBN: (纸本)9798350337488

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model's efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model's robustness and its potential to significantly contribute to VTE research. © 2023 IEEE.

关键词： BERT Bi-LSTM Deep Learning NLP Transfer Learning VTE

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings 23

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Bonnell, Jerry Wuchty, Stefan Ogihara, Mitsunori Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States University of Miami Department of Computer Science Coral GablesFL33146 United States

ISBN: (纸本)9798350374889

Accurate prediction of television (TV) audience ratings is a crucial technology for advertisers and broadcast-ers, which influences advertising costs and indicates program popularity. As for Japanese TV dramas, previous studies have examined factors available prior to broadcast, such as cast com-position, metadata variables (e.g., scheduled time slot, broadcasting station, episode duration), and indicators of actor popularity. However, such studies have been limited in their evaluation of contextual information related to these factors and have not considered additional modalities such as text data. Here, we propose the use of knowledge-based embeddings derived from Japanese Wikipedia to improve audience ratings prediction. The proposed bag-of-entities method is applied to represent dramas in terms of cast, production team, and plot synopsis elements, and is evaluated against conventional metadata features. In an ablation study, supervised forecasting models using Support Vector Machine (SVM) are trained on combinations of the feature groups. These are devised and tested on viewership data collected from the Audience Rating TV (ARTV) database for 574 Japanese TV dramas aired between 2003 and 2021. Our experimental results indicate that a SVM model conditioned on the full feature set outperforms baseline methods, achieving a 77.4 % F1-score. After stratifying predictions based on time slot popularity, the approach is found to be effective for predicting audience ratings in challenging, high-popularity time slots, with improvements in recognition rate up to 19.1 %. © 2024 IEEE.

关键词： Support vector machines

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Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Improving VTE Identification through Language Models from Ra...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Deng, Jamie Wu, Yusen Yesha, Yelena Nguyen, Phuong University of Miami Department of Computer Science MiamiFL United States University of Miami Frost Institute for Data Science and Computing MiamiFL United States University of Miami Department of Radiology MiamiFL United States

ISBN: (纸本)9798350386226

Venous thromboembolism (VTE) is a critical cardio-vascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches. © 2024 IEEE.

关键词： Deep learning

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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Foundation and Large Language Models (FLLM), International Conference on

作者： Rajesh Pasupuleti Ravi Vadapalli Christopher Mader Norris Timothy Frost Institute for Data Science and Computing (IDSC) University of Miami Coral Gables FL USA

ISBN: (数字)9798350354799

ISBN: (纸本)9798350354805

For the public, understanding Large Language Models (LLMs) can be likened to recognizing how a well-trained assistant works—one that has read an extensive library of information on virtually every topic imaginable. Imagine an assistant that not only reads and remembers all this information but also learns the nuances of how words and ideas are connected across different contexts. This assistant can then use this knowledge to write articles, answer questions, compose emails, or even generate creative stories, all in a manner that feels surprisingly human. This capability comes from what's known as "transformer architecture," a type of design that helps the model pay attention to different parts of the text as it reads, making it adept at understanding and generating language. LLMs are a breakthrough in technology because they can understand and produce language with a level of subtlety and complexity that was previously unachievable, making them valuable tools across various industries. The paper aims to provide a comprehensive analysis of the transformative impact of LLMs across various enterprise sectors. It intends to contribute to the understanding of how LLMs can enhance efficiency, innovation, and decision-making processes in industries such as healthcare, finance, education, and in the software engineering sector. It also provides a comprehensive overview of current popular LLMs in Enterprise applications, in various domains, and discusses the Ethical, Technical, and Regulatory challenges, future trends, and developments in this dynamic field.

关键词： Industries Technological innovation Image recognition Large language models Finance Medical services Transformers Market research Libraries Software engineering

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A Joint Communication and Learning Design for Secure Federated Learning with Differential Privacy 100

A Joint Communication and Learning Design for Secure Federat...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lin, Licheng Yang, Zhaohui Yang, Qianqian Chen, Mingzhe Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331517786

In this paper, the problem of resource allocation for non-orthogonal multiple access (NOMA) enabled secure federated learning (FL) is investigated. In the considered model, a set of users participate in the FL training through transmitting their trained FL model parameters to the base stations (BSs) via NOMA techniques. To prevent data leakage, each user uses the differential privacy (DP) technique through adding Gaussian noise to its FL model parameters. The problem of minimizing overall privacy leakage of all FL participaring users is formulated as an optimization problem through jointly optimizing the connections between users and BSs, transmit power of the users, and the DP noise power. To solve the formulated non-convex optimization problem, a genetic algorithm is proposed to search for feasible solutions in which user connection matrix is taken as gene and the objective function value is taken as the fitness of solution. Simulation results show that the proposed genetic algorithm reduces privacy leakage by up to 73% compared to the conventional alternating optimization algorithm. © 2024 IEEE.

关键词： Federated learning

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jerry Bonnell Stefan Wuchty Mitsunori Ogihara Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science University of Miami Coral Gables FL USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

关键词： Support vector machines Humanities TV Atmospheric modeling Production Encyclopedias Predictive models Metadata Internet Online services

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Cyber Security Issues and Challenges Related to Generative AI and ChatGPT

Cyber Security Issues and Challenges Related to Generative A...

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Rajesh Pasupuleti Ravi Vadapalli Christopher Mader Frost Institute for Data Science and Computing (IDSC) University of Miami Coral Gables FL USA IDSC Advanced Computing Systems Electrical and Computer Engineering (ECE) University of Miami Coral Gables FL USA Systems and Data Engineering Frost Institute for Data Science and Computing (IDSC) University of Miami Coral Gables FL USA

In recent years, Generative Artificial Intelligence (AI) and ChatGPT (Generative Pre-trained Transformer) models that are capable of generating realistic human-mimicked languages have gained progressive popularity. With the evolution of technology, there has been a significant increase in the availability and usage of artificial intelligence tools, such as ChatGPT and Generative AI, that will assist in shaping the future. However, this increasing popularity poses a potential risk if used inappropriately. Threats from AI pose special challenges for government, the private sector, and national security. In this paper, we address some of key concerns of significant cyber security issues and challenges related to Generative AI and ChatGPT. With careful consideration to application usage, organizations can implement appropriate security measures to mitigate these risks. We also incorporate recommendations about ChatGPT usage and its impact on society. It is important that researchers, developers, and policymakers (CIOs, CSOs) work together to mitigate these risks and to ensure that these models are used in a responsible and ethical manner.

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Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports

Improving VTE Identification through Adaptive NLP Model Sele...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jamie Deng Yusen Wu Hilary Hayssen Brain Englum Aman Kankaria Minerva Mayorga-Carlin Shalini Sahoo John Sorkin Brajesh Lal Yelena Yesha Phuong Nguyen Frost Institute for Data Science & Computing University of Miami FL USA School of Medicine University of Maryland MD USA

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model’s efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model’s robustness and its potential to significantly contribute to VTE research.

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Efficient data-Sketches and Fine-Tuning for Early Detection of Distributional Drift in Medical Imaging

arXiv

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arXiv 2024年

作者： Wu, Yusen Chen, Hao Makki, Alex Pissinou Nguyen, Phuong Yesha, Yelena Frost Institute for Data Science and Computing University of Miami MiamiFL United States Department of Radiology University of Miami MiamiFL United States

Distributional drift detection is important in medical applications as it helps ensure the accuracy and reliability of models by identifying changes in the underlying data distribution that could affect diagnostic or treatment decisions. However, current methods have limitations in detecting drift;for example, the inclusion of abnormal datasets can lead to unfair comparisons. This paper presents an accurate and sensitive approach to detect distributional drift in CT-scan medical images by leveraging data-sketching and fine-tuning techniques. We developed a robust baseline library model for real-time anomaly detection, allowing for efficient comparison of incoming images and identification of anomalies. Additionally, we fine-tuned a vision transformer pre-trained model to extract relevant features using breast cancer images as an example, significantly enhancing model accuracy to 99.11%. Combining with data-sketches and fine-tuning, our feature extraction evaluation demonstrated that cosine similarity scores between similar datasets provide greater improvements, from around 50% increased to 100%. Finally, the sensitivity evaluation shows that our solutions are highly sensitive to even 1% salt-and-pepper and speckle noise, and it is not sensitive to lighting noise (e.g., lighting conditions have no impact on data drift). The proposed methods offer a scalable and reliable solution for maintaining the accuracy of diagnostic models in dynamic clinical environments. © 2024, CC BY.

关键词： Medical imaging

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