DLASE: A light-weight framework supporting Deep Learning for Edge Devices

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

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Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Head & Face Medicine ◽

10.1186/s13005-021-00292-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Daniel G. E. Thiem ◽

Paul Römer ◽

Matthias Gielisch ◽

Bilal Al-Nawas ◽

Martin Schlüter ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Hyperspectral Imaging ◽

Oral Mucosa ◽

Deep Neural Network ◽

Light Weight ◽

Tissue Classification ◽

Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

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Towards Light-Weight Deep Learning Based Malware Detection

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) ◽

10.1109/compsac.2018.00092 ◽

2018 ◽

Cited By ~ 4

Author(s):

Zeliang Kan ◽

Haoyu Wang ◽

Guoai Xu ◽

Yao Guo ◽

Xiangqun Chen

Keyword(s):

Deep Learning ◽

Malware Detection ◽

Light Weight

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A Deep-Learning-Driven Light-Weight Phishing Detection Sensor

Sensors ◽

10.3390/s19194258 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4258 ◽

Cited By ~ 12

Author(s):

Bo Wei ◽

Rebeen Ali Hamad ◽

Longzhi Yang ◽

Xuan He ◽

Hao Wang ◽

...

Keyword(s):

Deep Learning ◽

Energy Saving ◽

Real Time ◽

Learning Algorithm ◽

Rapid Development ◽

Social Engineering ◽

Machine Learning Techniques ◽

Light Weight ◽

Learning Techniques ◽

Phishing Detection

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

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