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Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions
[article]
2022
arXiv
pre-print
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AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. ...
We found that 1) Defence techniques for adversarial attacks on AI are available for specific kind of attacks and even adversarial training is AI based technique which in further prone to different attacks ...
Adversarial text is a perturbation in the text regarding semantics, syntax, and visual similarity, which will mislead NLP. The Figure 11 depicts the methods for generating adversarial text. ...
arXiv:2206.04793v1
fatcat:v2wrluwugja65btmjct5wlrfm4
VLSP 2021 - vnNLI Challenge: Vietnamese and English-Vietnamese Textual Entailment
2022
VNU Journal of Science Computer Science and Communication Engineering
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for the task. ...
This paper presents the first challenge on recognizing textual entailment (RTE), also known as natural language inference (NLI), held in a Vietnamese Language and Speech Processing workshop (VLSP 2021) ...
Therefore, in VLSP 2021 we have decided to launch the first shared task for Vietnamese and English-Vietnamese Textual Entailment. ...
doi:10.25073/2588-1086/vnucsce.363
fatcat:dm23nhvuvne6tjlzipvkm6stri
Deep transfer learning for automatic speech recognition: Towards better generalization
2023
Knowledge-Based Systems
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2023 accepted arXiv:2304.14535v2 fatcat:6m4izsigcjgj7gqh3u6tz5qupuOther Versions
Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research. ...
Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. ...
AUC=79% No [188] RNN Clinical depression recognition Feature trans. ...
doi:10.1016/j.knosys.2023.110851
fatcat:r5p53jicxbd7vpcdakp6selkfa
A Study on Extracting Named Entities from Fine-tuned vs. Differentially Private Fine-tuned BERT Models
[article]
2022
arXiv
pre-print
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We search in these samples for named entities and check if they are also present in the fine-tuning datasets. We experiment with two benchmark datasets in the domains of emails and blogs. ...
We create a large number of text samples from the fine-tuned BERT models utilizing a custom sequential sampling strategy with two prompting strategies. ...
In total, we generated 20,000 text samples for each setup.
Named Entity Recognition. ...
arXiv:2212.03749v1
fatcat:qmpqtpdurra35dcdurhofxgmla
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2110.05006v3 fatcat:2dtoy4d6ozcn3ew5hcfmbkxikiOther Versions
Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. ...
This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic ...
To facilitate the development of methods for text inference and entailment in the medical domain, participants in the MEDIQA 2019 shared task [3] investigated the SciBERT, BioBERT, and ClinicalBERT in ...
arXiv:2110.05006v4
fatcat:tgz5zgzmizasphlgsxur3kereu
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
2024
Bioengineering
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, thus presenting a pioneering solution for clinical practice. ...
In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making. ...
for biological signals or clinical text data). ...
doi:10.3390/bioengineering11030219
pmid:38534493
pmcid:PMC10967767
fatcat:tdrqch5tinhhbmnk7bkf4ilsv4
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation
2024
Patterns
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2023 pre-print arXiv:2305.06217v1 fatcat:jmaeecubwbanvkg4x47y26a2dyOther Versions
We argue that C4 approaches could pave the way for ML models that are both holistic and widely applicable. ...
This review studies the utility of cross-cohort cross-category (C4) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. ...
The challenge entails securely modeling the relationships between different modalities, inferring, and integrating them to construct models that generalize across multiple sites without sharing raw data ...
doi:10.1016/j.patter.2023.100913
pmid:38370129
pmcid:PMC10873158
fatcat:gibxi7ta2fhnxa55oc6srlvaha
Deep Learning in Science
[article]
2020
arXiv
pre-print
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interdisciplinary DL applications to disciplinary research within application domains. ...
However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way. ...
DL-based cross domain recommender systems, for instance, offer high-quality cross domain recommendation by exploiting numeric measurements, images, text and interactions in a unified joint framework. ...
arXiv:2009.01575v2
fatcat:4ttqgjdjfjbydp7flnhcgg5p7m
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2012.09110v2 fatcat:5ki3bibid5as7nq7ztma5lja64Other Versions
We believe such rigorous analysis will provide a baseline for future research in the domain. ...
Globally there are calls for technology to be made more humane and human-centered. ...
Adversarial Attacks This challenge has been recognized and discussed either for crafting fake data that could belong to different domains; text [72] , images [73] , audio [68] , network signals [74 ...
arXiv:2012.09110v4
fatcat:yxh5tvpehbgldcblweoovbvdsq
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
2022
ACM Transactions on Computing for Healthcare
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Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data ...
This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries. ...
Genomics is a critical domain for privacy-aware modeling, especially in precision medicine. ...
doi:10.1145/3533708
fatcat:iktrdcf6vrdx5daupes2t4tove
A Review on Text-Based Emotion Detection – Techniques, Applications, Datasets, and Future Directions
[article]
2022
arXiv
pre-print
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With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. ...
It also reviews the different applications of TBED across various research domains and highlights its use. ...
Their method uses shared-private networks to extract both domain-invariant and domain-specific features, then uses the extracted features to train two classifiers. ...
arXiv:2205.03235v1
fatcat:b3m25fg6xfc3leeym22eqysq5a
Lightweight Transformers for Clinical Natural Language Processing
[article]
2023
arXiv
pre-print
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In this work, we specifically focus on development of compact language models for processing clinical texts (i.e. progress notes, discharge summaries etc). ...
Our extensive evaluation was done across several standard datasets and covered a wide range of clinical text-mining tasks, including Natural Language Inference, Relation Extraction, Named Entity Recognition ...
The data used for this research were obtained from ISARIC4C. ...
arXiv:2302.04725v1
fatcat:da4fspyx4bcbvb2qn2533bdhry
Federated Distillation: A Survey
[article]
2024
arXiv
pre-print
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Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. ...
Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. ...
Image Recognition. The FD framework has also been explored in the domain of image recognition. ...
arXiv:2404.08564v1
fatcat:cktkf3zn4zeqjbvfx7uouiwrbq
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
[article]
2022
arXiv
pre-print
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We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains. ...
While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed Sponge Attacks against AI models aim ...
Examples of domains in which collaborative Human-AI systems have been studied include data science [11] , clinical domains [10] , and creative tasks [12] . ...
arXiv:2212.03980v1
fatcat:b4zpikkzz5flljgoxlj76mwdiy
A Roadmap for Big Model
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.14101v3 fatcat:m7pyxdvamfdndaipb3lew3syhyOther Versions
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. ...
In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies ...
Relatively speaking, there are lots of training data for speech recognition and text translation. ...
arXiv:2203.14101v4
fatcat:rdikzudoezak5b36cf6hhne5u4
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