PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System

Sun, Le; Zhong, Zhaoyi; Qu, Zhiguo; Xiong, Naixue

doi:10.1109/jbhi.2022.3145999

Cited by 33 publications

(10 citation statements)

References 55 publications

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“…The practical application of the existing legal judgment prediction technology in the judicial field and its related products and solutions are gradually entering the public eye and being applied to assist the actual judicial process [ 5 ]. Legal judgment prediction technology is not intended to directly replace judges in adjudicating cases.…”

Section: Introductionmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

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Section: Introductionmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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“…Next, the conditional distribution, based on the principles of conditioning the joint Gaussian prior distribution on the observations [30], is calculated as ( 21) through (23), where…”

Section: Ibbd: Iomt Big-data Bayesian-backward Deep-encoder Learningmentioning

confidence: 99%

“…al [2] reported a Kmeans-based dictionary approach to compress the ECG data. The deep autoencoder (AE) was reported to compress the ECG signal in [23]. Abdellatif et.…”

Section: Introductionmentioning

confidence: 99%

Efficient IoT Big Data Streaming With Deep-Learning-Enabled Dynamics

Wong

Piuri

Scotti

et al. 2023

IEEE Internet Things J.

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Internet of Medical Things (IoMT) is igniting many emerging smart health applications, by continuously streaming the big data for data-driven innovations. One critical obstacle in IoMT big data is the power hungriness of long-term data transmission. Targeting this challenge, we propose a novel framework called, IoMT Big-data Bayesian-backward Deepencoder learning (IBBD), which mines deep autoencoder (AE) configurations for data sparsification and determines optimal trade-offs between information loss and power overhead. More specifically, the IBBD framework leverages an additional external Bayesian-backward loop that recommends AE configurations, on top of a traditional deep learning loop that executes and evaluate the AE quality. The IBBD recommendation is based on confidence to further minimize the regularized metrics that quantify the quality of AE configurations, and it further leverages regularization techniques to allow adjusting error-power tradeoffs in the mining process. We have conducted thorough experiments on a cardiac data streaming application and demonstrated the superiority of IBBD over the common practices like Discrete Wavelet Transform, and we have further generalized IBBD through validating the optimal AE configurations determined on one user to other users. This study is expected to greatly advance IoMT big data streaming practices towards precision medicine.

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“…However, the construction of emotional dictionary is timeconsuming, laborious, and difficult to maintain. Artificial feature extraction needs expert domain knowledge (Sun et al, 2022). Word vector technology, which has sprung up in recent years, has become the basic technology in natural language processing.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Text Emotion Analysis for Legal Anomie

She

2022

Front. Psychol.

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Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.

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PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System

Cited by 33 publications

References 55 publications

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Efficient IoT Big Data Streaming With Deep-Learning-Enabled Dynamics

Deep Learning-Based Text Emotion Analysis for Legal Anomie

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