Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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Two-phase SPH simulation of vertical water entry of a two-dimensional structure

Journal of Hydrodynamics ◽

10.1007/s42241-018-0102-x ◽

2018 ◽

Vol 30 (5) ◽

pp. 841-850 ◽

Cited By ~ 1

Author(s):

Lin Ma ◽

Xi Zhao ◽

Hua Liu

Keyword(s):

Water Entry ◽

Dimensional Structure ◽

Two Dimensional ◽

Two Phase ◽

Sph Simulation

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Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Remote Sensing ◽

10.3390/rs12030548 ◽

2020 ◽

Vol 12 (3) ◽

pp. 548 ◽

Cited By ~ 1

Author(s):

Xinzheng Zhang ◽

Guo Liu ◽

Ce Zhang ◽

Peter M. Atkinson ◽

Xiaoheng Tan ◽

...

Keyword(s):

Deep Learning ◽

Change Detection ◽

Speckle Noise ◽

Sar Image ◽

Phase Object ◽

Sar Images ◽

Two Phase ◽

Object Based ◽

Multi Temporal ◽

Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

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Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

AI ◽

10.3390/ai1020010 ◽

2020 ◽

Vol 1 (2) ◽

pp. 166-179 ◽

Cited By ~ 4

Author(s):

Ziyang Tang ◽

Xiang Liu ◽

Hanlin Chen ◽

Joseph Hupy ◽

Baijian Yang

Keyword(s):

Deep Learning ◽

High Resolution ◽

Object Detection ◽

Aerial Images ◽

Unmanned Aerial Systems ◽

Computer Assisted ◽

Visual Interpretation ◽

Two Phase ◽

Aerial Systems ◽

Spot Fires

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

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Ultra-Wideband Antenna-Induced Error Prediction Using Deep Learning on Channel Response Data

2017 IEEE 85th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2017.8108571 ◽

2017 ◽

Cited By ~ 3

Author(s):

Janis Tiemann ◽

Johannes Pillmann ◽

Christian Wietfeld

Keyword(s):

Deep Learning ◽

Ultra Wideband ◽

Error Prediction ◽

Wideband Antenna ◽

Channel Response ◽

Response Data

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Two-Phase Deep Learning-Based EDoS Detection System

Applied Sciences ◽

10.3390/app112110249 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10249

Author(s):

Chien-Nguyen Nhu ◽

Minho Park

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Network Traffic ◽

Input Data ◽

Short Term Memory ◽

Detection System ◽

Scale Up ◽

Sequence Length ◽

Two Phase ◽

Detection Scheme

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic; however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

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Local Post-hoc Explainable Methods for Adversarial Text Attacks

10.36227/techrxiv.17185568.v1 ◽

2021 ◽

Author(s):

Yidong Chai ◽

Ruicheng Liang ◽

Hongyi Zhu ◽

Sagar Samtani ◽

Meng Wang ◽

...

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Language Processing ◽

Black Box ◽

Learning Models ◽

Two Phase ◽

Sensitivity Estimation ◽

Execution Phase ◽

Adversarial Examples ◽

Post Hoc

Deep learning models have significantly advanced various natural language processing tasks. However, they are strikingly vulnerable to adversarial text attacks, even in the black-box setting where no model knowledge is accessible to hackers. Such attacks are conducted with a two-phase framework: 1) a sensitivity estimation phase to evaluate each element’s sensitivity to the target model’s prediction, and 2) a perturbation execution phase to craft the adversarial examples based on estimated element sensitivity. This study explored the connections between the local post-hoc explainable methods for deep learning and black-box adversarial text attacks and proposed a novel eXplanation-based method for crafting Adversarial Text Attacks (XATA). XATA leverages local post-hoc explainable methods (e.g., LIME or SHAP) to measure input elements’ sensitivity and adopts the word replacement perturbation strategy to craft adversarial examples. We evaluated the attack performance of the proposed XATA on three commonly used text-based datasets: IMDB Movie Review, Yelp Reviews-Polarity, and Amazon Reviews-Polarity. The proposed XATA outperformed existing baselines in various target models, including LSTM, GRU, CNN, and BERT. Moreover, we found that improved local post-hoc explainable methods (e.g., SHAP) lead to more effective adversarial attacks. These findings showed that when researchers constantly advance the explainability of deep learning models with local post-hoc methods, they also provide hackers with weapons to craft more targeted and dangerous adversarial attacks.

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