scholarly journals Improving Text-to-Code Generation with Features of Code Graph on GPT-2

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2706
Author(s):  
Incheon Paik ◽  
Jun-Wei Wang
Keyword(s):  
Performance Improvement ◽  
Code Generation ◽  
Data Stream ◽  
Fine Tuning ◽  
Learning Models ◽  
Experimental Phase ◽  
Recent Approach ◽  
Great Performance ◽  
Semantic Tree ◽  
Code Graph

Code generation, as a very hot application area of deep learning models for text, consists of two different fields: code-to-code and text-to-code. A recent approach, GraphCodeBERT uses code graph, which is called data flow, and showed good performance improvement. The base model architecture of it is bidirectional encoder representations from transformers (BERT), which uses the encoder part of a transformer. On the other hand, generative pre-trained transformer (GPT)—another multiple transformer architecture—uses the decoder part and shows great performance in the multilayer perceptron model. In this study, we investigate the improvement of code graphs with several variances on GPT-2 to refer to the abstract semantic tree used to collect the features of variables in the code. Here, we mainly focus on GPT-2 with additional features of code graphs that allow the model to learn the effect of the data stream. The experimental phase is divided into two parts: fine-tuning of the existing GPT-2 model, and pre-training from scratch using code data. When we pre-train a new model from scratch, the model produces an outperformed result compared with using the code graph with enough data.

Parsing of Urban Facades from 3D Point Clouds Based on a Novel Multi-View Domain

2021 ◽  
Vol 87 (4) ◽  
pp. 283-293
Author(s):  
Wei Wang ◽  
Yuan Xu ◽  
Yingchao Ren ◽  
Gang Wang
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Performance Improvement ◽  
Data Distribution ◽  
Point Clouds ◽  
Learning Models ◽  
Data Set ◽  
3D Point Clouds ◽  
Segmentation Accuracy ◽  
The Mean

Recently, performance improvement in facade parsing from 3D point clouds has been brought about by designing more complex network structures, which cost huge computing resources and do not take full advantage of prior knowledge of facade structure. Instead, from the perspective of data distribution, we construct a new hierarchical mesh multi-view data domain based on the characteristics of facade objects to achieve fusion of deep-learning models and prior knowledge, thereby significantly improving segmentation accuracy. We comprehensively evaluate the current mainstream method on the RueMonge 2014 data set and demonstrate the superiority of our method. The mean intersection-over-union index on the facade-parsing task reached 76.41%, which is 2.75% higher than the current best result. In addition, through comparative experiments, the reasons for the performance improvement of the proposed method are further analyzed.

scholarly journals Covid-19 detection via deep neural network and occlusion sensitivity maps

2021 ◽  
Author(s):  
Noor Ahmad ◽  
Muhammad Aminu ◽  
Mohd Halim Mohd Noor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Color Images ◽  
Fine Tuning ◽  
Training Dataset ◽  
Learning Approaches ◽  
Learning Models ◽  
Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

Diesel Engine Performance Improvement for Constant Speed Application Using CFD

2017 ◽  
Author(s):  
Balasaheb S. Dahifale ◽  
Anand S. Patil
Keyword(s):  
Combustion Chamber ◽  
Fuel Consumption ◽  
Performance Improvement ◽  
Test Data ◽  
Engine Performance ◽  
Exhaust Valve ◽  
Fine Tuning ◽  
Intake Valve ◽  
Valve Seat ◽  
Load Conditions

The detailed investigation of flow behavior inside the combustion chamber and performance of engine is most challenging problem due to constraints in Experimental Data collection during testing; However, Experimental testing is essential for establishment of correlation with CFD Predictions. Hence, the baseline engine was tested at different load conditions and validated with CFD results, before it was optimized for performance improvement. The objective of the CFD Prediction was not only to optimize performance (Fuel Efficiency, Power, Torque, etc.) & Emissions Reduction, but also to assess feasibility of Performance Upgrade Potential. In the present CFD study, surface mesh and domain was prepared for the flame face, intake valve, intake valve seat, exhaust valve, exhaust valve seat and liner for closed volume cycle, between IVC and EVO using CFD code VECTIS. Finally simulations for three different load conditions were conducted using VECTIS solver. Initially, in-cylinder pressure vis a vis crank angle prediction was carried out for 100%, 75% and 50% load conditions. Then the fine tuning of (P-ϴ) diagram for different load conditions was conducted by varying different combustion parameters. Further, the engine performance validation was carried out for rated and part load conditions in terms of, IMEP, BMEP, break specific fuel consumption and power output, while NOx mass fractions were used to convert the NOx to g/kWh for comparison of emission levels with the test data. Finally optimized re-entrant combustion chamber and modified valve timing with optimum fuel injection system simulation was carried out to achieve target performance with reduced fuel consumption. A 3D CFD result showed reduction in BSFC and was in close agreement with the test data.

Abstract P319: Can Deep Learning Find the Ischemic Core on CT? Transfer Learning From Pre-Trained MRI-Based Networks

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Yannan Yu ◽  
Soren Christensen ◽  
Yuan Xie ◽  
Enhao Gong ◽  
Maarten G Lansberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Learning Model ◽  
Fine Tuning ◽  
Learning Models ◽  
Starting Point ◽  
Stroke Lesion ◽  
Ischemic Core ◽  
Deep Learning Model

Objective: Ischemic core prediction from CT perfusion (CTP) remains inaccurate compared with gold standard diffusion-weighted imaging (DWI). We evaluated if a deep learning model to predict the DWI lesion from MR perfusion (MRP) could facilitate ischemic core prediction on CTP. Method: Using the multi-center CRISP cohort of acute ischemic stroke patient with CTP before thrombectomy, we included patients with major reperfusion (TICI score≥2b), adequate image quality, and follow-up MRI at 3-7 days. Perfusion parameters including Tmax, mean transient time, cerebral blood flow (CBF), and cerebral blood volume were reconstructed by RAPID software. Core lab experts outlined the stroke lesion on the follow-up MRI. A previously trained MRI model in a separate group of patients was used as a starting point, which used MRP parameters as input and RAPID ischemic core on DWI as ground truth. We fine-tuned this model, using CTP parameters as input, and follow-up MRI as ground truth. Another model was also trained from scratch with only CTP data. 5-fold cross validation was used. Performance of the models was compared with ischemic core (rCBF≤30%) from RAPID software to identify the presence of a large infarct (volume>70 or >100ml). Results: 94 patients in the CRISP trial met the inclusion criteria (mean age 67±15 years, 52% male, median baseline NIHSS 18, median 90-day mRS 2). Without fine-tuning, the MRI model had an agreement of 73% in infarct >70ml, and 69% in >100ml; the MRI model fine-tuned on CT improved the agreement to 77% and 73%; The CT model trained from scratch had agreements of 73% and 71%; All of the deep learning models outperformed the rCBF segmentation from RAPID, which had agreements of 51% and 64%. See Table and figure. Conclusions: It is feasible to apply MRP-based deep learning model to CT. Fine-tuning with CTP data further improves the predictions. All deep learning models predict the stroke lesion after major recanalization better than thresholding approaches based on rCBF.

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

2018 ◽  
pp. 200-221
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam
Keyword(s):  
Big Data ◽  
Real Time ◽  
Performance Improvement ◽  
Data Stream ◽  
Real Data ◽  
Stream Computing ◽  
Time Data ◽  
Real Time Data ◽  
Computing Platforms ◽  
Time And Energy

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

scholarly journals Covid-19 detection via deep neural network and occlusion sensitivity maps

2021 ◽  
Author(s):  
Noor Ahmad ◽  
Muhammad Aminu ◽  
Mohd Halim Mohd Noor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Color Images ◽  
Fine Tuning ◽  
Training Dataset ◽  
Learning Approaches ◽  
Learning Models ◽  
Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

scholarly journals A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Morshedul Bari Antor ◽  
A. H. M. Shafayet Jamil ◽  
Maliha Mamtaz ◽  
Mohammad Monirujjaman Khan ◽  
Sultan Aljahdali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Fine Tuning ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Alzheimer’s disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer’s is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer’s disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

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