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Library for online handwriting recognition system using UNIPEN database.

, 2 May 2012
a library for handwriting recognition system which can recognize 99% to digit or 90% to capital letter+ digit
capital_letters__digit_89_.zip
lowcase_letter_89_.zip
numberic_97_.zip
UNIPENViewer_demo_source_version_1.01.zip
UNIPENViewer demo source version 1.01
Neurons.dll
NNControl
bin
Debug
Neurons.dll
Neurons.pdb
NNControl.dll
NNControl.pdb
UPImage.dll
UPImage.pdb
Release
Common
NNTraining
obj
Debug
DesignTimeResolveAssemblyReferences.cache
DesignTimeResolveAssemblyReferencesInput.cache
GenerateResource.read.1.tlog
GenerateResource.write.1.tlog
NNControl.Common.UPTemplateControl.resources
NNControl.dll
NNControl.FlashForm.resources
NNControl.NNTraining.UP_NNTrainingControl.resources
NNControl.pdb
NNControl.Properties.Resources.resources
NNControl.TrainingParametersForm.resources
NNControl.UPViewer.UpImageViewer.resources
ResolveAssemblyReference.cache
TempPE
Properties.Resources.Designer.cs.dll
UP-NeuralTraining.dll
UP-NeuralTraining.pdb
UPControl.Common.BaseControl.resources
UPControl.Common.UPTemplateControl.resources
UPControl.FlashForm.resources
UPControl.NNTraining.UP_NNTrainingControl.resources
UPControl.TrainingParametersForm.resources
UPControl.UPViewer.UpImageViewer.resources
UP_NeuralTraining.FlashForm.resources
UP_NeuralTraining.TrainingParametersForm.resources
UP_NeuralTraining.UP_NNTrainingControl.resources
Release
DesignTimeResolveAssemblyReferences.cache
DesignTimeResolveAssemblyReferencesInput.cache
GenerateResource.read.1.tlog
GenerateResource.write.1.tlog
NNControl.Common.UPTemplateControl.resources
NNControl.dll
NNControl.FlashForm.resources
NNControl.NNTraining.UP_NNTrainingControl.resources
NNControl.pdb
NNControl.Properties.Resources.resources
NNControl.TrainingParametersForm.resources
NNControl.UPViewer.UpImageViewer.resources
ResolveAssemblyReference.cache
TempPE
Properties.Resources.Designer.cs.dll
Properties
Resources
btnBack.png
btnDrag.png
btnFitToScreen.png
btnNext.png
btnOpen.png
btnPreview.png
btnRotate270.png
btnRotate90.png
btnSelect.png
btnZoomIn.png
btnZoomOut.png
cry.png
Drag.cur
file.png
folder-open.png
folder.png
folders_explorer.png
Grab.cur
home.png
label-link.png
script_(stop).gif
smile.png
Stop sign.png
Upload.png
UPViewer
UNIPENviewer
UNIPENviewer.suo
bin
Debug
Config
Neurons.dll
Neurons.pdb
NNControl.dll
NNControl.pdb
UNIPENviewer.exe
UNIPENviewer.pdb
UNIPENviewer.vshost.exe
UNIPENviewer.vshost.exe.manifest
UPImage.dll
UPImage.pdb
Release
Config
obj
Debug
DesignTimeResolveAssemblyReferences.cache
DesignTimeResolveAssemblyReferencesInput.cache
GenerateResource.read.1.tlog
GenerateResource.write.1.tlog
ResolveAssemblyReference.cache
TempPE
UNIPENviewer.exe
UNIPENviewer.MainForm.resources
UNIPENviewer.pdb
UNIPENviewer.Properties.Resources.resources
Release
DesignTimeResolveAssemblyReferencesInput.cache
GenerateResource.read.1.tlog
GenerateResource.write.1.tlog
ResolveAssemblyReference.cache
TempPE
UNIPENviewer.exe
UNIPENviewer.MainForm.resources
UNIPENviewer.pdb
UNIPENviewer.Properties.Resources.resources
x86
Debug
DesignTimeResolveAssemblyReferences.cache
DesignTimeResolveAssemblyReferencesInput.cache
GenerateResource.read.1.tlog
GenerateResource.write.1.tlog
ResolveAssemblyReference.cache
TempPE
UNIPENviewer.exe
UNIPENviewer.Form1.resources
UNIPENviewer.pdb
UNIPENviewer.Properties.Resources.resources
Properties
Settings.settings
UPUnipen
UPImage.dll
UNIPENViewer_demo_version_1.01.zip
UNIPENViewer demo version 1.01
Config
Neurons.dll
NNControl.dll
UNIPENviewer.exe
UNIPENviewer.vshost.exe
UPImage.dll
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Drawing;
using System.Data;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Threading;
using System.Threading.Tasks;
using UPImage;
using UPImage.Data;
using Neurons;
using NNControl.Common;
using System.IO;
using System.Drawing.Imaging;
using ArchiveSerialization;
namespace NNControl.NNTraining
{
   
    public partial class UP_NNTrainingControl : Common.UPTemplateControl
    {
        bool isDatabaseReady;
        bool isTrainingRuning;
        bool isCancel;
        UPImage.Data.UPDataProvider dataProvider;
        bool preview;
        NetworkParameters nnParameters;
        ConvolutionNetwork network;
        CancellationTokenSource tokenSource;
        CancellationToken token;
        String nntfile;
        private List<Char> Letters2 = new List<Char>(36) { 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
        'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
        '0', '1', '2', '3', '4', '5', '6', '7', '8', '9' };
        private List<Char> Letters = new List<Char>(62) { 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',
        'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
        'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r',
        's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9' };
        private List<Char> Letters1 = new List<Char>(10) { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9' };
        private List<Char> Letters3 = new List<Char>(26) { 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r',
        's', 't', 'u', 'v', 'w', 'x', 'y', 'z'};
        Task maintask;
        public UP_NNTrainingControl():base()
        {
            InitializeComponent();
            preview = true;
            nnParameters = new NetworkParameters();
            network = null;
            maintask = null;
            Initialization();
        }
        private void Initialization()
        {
            isDatabaseReady = false;
            isTrainingRuning = false;
            isCancel = false;
            dataProvider = new UPImage.Data.UPDataProvider();
            tokenSource = new CancellationTokenSource();
            token = tokenSource.Token;
            network = null;
            nntfile = null;
        }
        protected override void AddObject(int iCondition, object value)
        {
            switch (iCondition)
            {
                case 0:
                    //show getting image data commments
                    this.btnOpen.Image = global::NNControl.Properties.Resources.script__stop_;
                    btTrain.Enabled = false;
                    lbCommend.Items.Add((String)value);
                    toolStripProgressBar1.Visible = true;
                    if (stopwatch.IsRunning)
                    {
                        // Stop the timer; show the start and reset buttons.
                        stopwatch.Stop();
                    }
                    else
                    {
                        // Start the timer; show the stop and lap buttons.
                        stopwatch.Reset();
                        stopwatch.Start();
                    }
                    break;
                case 1:
                    //loading image data successfully
                    this.btnOpen.Image = global::NNControl.Properties.Resources.btnOpen;
                    btTrain.Enabled = true;
                    lbCommend.Items.Add((String)value);
                    isDatabaseReady = true;
                    toolStripProgressBar1.Visible = false;
                    if (stopwatch.IsRunning)
                    {
                        // Stop the timer; show the start and reset buttons.
                        stopwatch.Stop();
                    }
                    break;
                case 2:
                    // backpropagation process...
                    lbCommend.Items.Add((String)value);
                    break;
                case 3:
                    //Caculation of Hessian 
                    int vl = (int)value;
                    lbCompleteRatio.Text = String.Format("{0} %", vl);
                    progressBar.Value = (int)value;
                    break;
                case 4:
                    // backpropagation completed...
                    lbCommend.Items.Add((String)value);
                    BackPropagationThreadsFinished();
                    break;
                case 98:
                    //cancel loading image data
                    this.btnOpen.Image = global::NNControl.Properties.Resources.btnOpen;
                    lbCommend.Items.Add((String)value);
                    isDatabaseReady = false;
                    dataProvider.Dispose();
                    dataProvider = new UPDataProvider();
                    toolStripProgressBar1.Visible = false;
                    if (stopwatch.IsRunning)
                    {
                        // Stop the timer; show the start and reset buttons.
                        stopwatch.Stop();
                    }
                    break;
                case 99:
                    //loading image data
                    toolStripProgressBar1.Value = (int)value;
                    break;
            }
        }
        protected override void AddObjects(int iCondition, object[] values)
        {
            String comment = "";
            int ratio = 0;
            switch (iCondition)
            {
                case 0:
                    comment = (String)values[0];
                    lbCommend.Items.Add(comment);
                    break;
                case 98:
                    TrainingResult result = (TrainingResult)values[0];
                    ListViewItem item1 = new ListViewItem();
                    item1.Text = result.Epoch.ToString();
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.CurrentMSE.ToString()));
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.MisPattern.ToString()));
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.Duration.ToString()));
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.Ratio.ToString()));
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.EtaLearningRate.ToString()));
                    item1.SubItems.Add(new ListViewItem.ListViewSubItem(item1, result.Distored.ToString()));
                    lvResult.Items.Add(item1);
                    String st = (String)values[1];
                    lbCommend.Items.Add(st);
                    //save nnt file
                    if (nntfile != null)
                    {
                        var fsIn = File.OpenWrite(nntfile);
                        var arIn = new Archive(fsIn, ArchiveOp.store);
                        network.Serialize(arIn);
                        fsIn.Close();
                    }
                    break;
                case 99:
                    ratio = (int)values[0];
                    progressBar.Value = ratio;
                    int pattern = (int)values[1];
                    lbPatternNo.Text = pattern.ToString();
                    uint misCount = (uint)values[2];
                    labelMisCount.Text = misCount.ToString();
                    double dMSE = (double)values[3];
                    labelMSE.Text = dMSE.ToString();
                    int pt = (int)values[5];
                    lbCompleteRatio.Text = String.Format("{0} %", ratio);
                    lbAccurate.Text = String.Format("{0} %", (double)(pattern - misCount)*100 / pattern);
                    if (pattern % 20 == 0)
                    {
                        char label = (char)values[4];
                        labelChar.Text = label.ToString();
                        byte[] data = dataProvider.ByteImagePatterns[pt].Image;
                        Bitmap bmp = CopyDataToBitmap(data, new Size(29, 29));
                        pictureBox1.Image = (Image)bmp;
                    }
                    break;
            }
        }
        private void btnPreview_Click(object sender, EventArgs e)
        {
            if (preview)
            {
                spcMain.Panel2Collapsed = true;
                preview = false;
            }
            else
            {
                spcMain.Panel2Collapsed = false;
                preview = true;
            }
        } 
        private void btnOpen_Click(object sender, EventArgs e)
        {
            if (dataProvider.IsDataStop == true)
            {
                try
                {
                    FolderBrowserDialog fbd = new FolderBrowserDialog();
                    // Show the FolderBrowserDialog.
                    DialogResult result = fbd.ShowDialog();
                    if (result == DialogResult.OK)
                    {
                        bool fn = false;
                        string folderName = fbd.SelectedPath;
                        Task[] tasks = new Task[2];
                        isCancel = false;
                        tasks[0] = Task.Factory.StartNew(() =>
                        {
                            dataProvider.IsDataStop = false;
                            this.Invoke(DelegateAddObject, new object[] { 0, "Getting image training data, please be patient...." });
                            dataProvider.GetPatternsFromFiles(folderName); //get patterns with default parameters
                            dataProvider.IsDataStop = true;
                            if (!isCancel)
                            {
                                this.Invoke(DelegateAddObject, new object[] { 1, "Congatulation! Image training data loaded succesfully!" });
                                dataProvider.Folder.Dispose();
                                isDatabaseReady = true;
                            }
                            else
                            {
                                this.Invoke(DelegateAddObject, new object[] { 98, "Sorry! Image training data loaded fail!" });
                            }
                            fn = true;
                        });
                        tasks[1] = Task.Factory.StartNew(() =>
                        {
                            int i = 0;
                            while (!fn)
                            {
                                Thread.Sleep(100);
                                this.Invoke(DelegateAddObject, new object[] { 99, i });
                                i++;
                                if (i >= 100)
                                    i = 0;
                            }

                        });

                    }


                }
                catch (Exception ex)
                {
                    MessageBox.Show(ex.ToString());
                }
            }
            else
            {
                DialogResult result = MessageBox.Show("Do you really want to cancel this process?", "Cancel loadding Images", MessageBoxButtons.YesNo);
                if (result == DialogResult.Yes)
                {
                    dataProvider.IsDataStop = true;
                    isCancel = true;
                }
            }
        }
        void CreateNetwork()
        {
            network = new ConvolutionNetwork();
            //layer 0: inputlayer
            network.Layers = new NNLayer[6];
            network.LayerCount = 6;
            NNLayer layer = new NNLayer("00-Layer Input", null, new Size(29, 29), 1, 5);
            network.InputDesignedPatternSize = new Size(29, 29);
            layer.Initialize();
            network.Layers[0] = layer;
            layer = new NNLayer("01-Layer ConvolutionalSubsampling", layer, new Size(13, 13), 10, 5);
            layer.Initialize();
            network.Layers[1] = layer;
            layer = new NNLayer("02-Layer ConvolutionalSubsampling", layer, new Size(5, 5), 60, 5);
            layer.Initialize();
            network.Layers[2] = layer;
            layer = new NNLayer("03-Layer FullConnected", layer, new Size(1, 200), 1, 5);
            layer.Initialize();
            network.Layers[3] = layer;
            layer = new NNLayer("04-Layer FullConnected", layer, new Size(1, 100), 1, 5);
            layer.Initialize();
            network.Layers[4] = layer;
            layer = new NNLayer("05-Layer FullConnected", layer, new Size(1, Letters3.Count), 1, 5);
            layer.Initialize();
            network.Layers[5] = layer;
            network.TagetOutputs = Letters3;
        
        }
        void CreateNetwork1()
        {
            network = new ConvolutionNetwork();
            //layer 0: inputlayer
            network.Layers = new NNLayer[5];
            network.LayerCount = 5;
            NNLayer layer = new NNLayer("00-Layer Input", null, new Size(29, 29), 1, 5);
            network.InputDesignedPatternSize = new Size(29, 29);
            layer.Initialize();
            network.Layers[0] = layer;
            layer = new NNLayer("01-Layer ConvolutionalSubsampling", layer, new Size(13, 13), 6, 5);
            layer.Initialize();
            network.Layers[1] = layer;
            layer = new NNLayer("02-Layer ConvolutionalSubsampling", layer, new Size(5, 5), 50, 5);
            layer.Initialize();
            network.Layers[2] = layer;
            layer = new NNLayer("03-Layer FullConnected", layer, new Size(1, 100), 1, 5);
            layer.Initialize();
            network.Layers[3] = layer;
            layer = new NNLayer("04-Layer FullConnected", layer, new Size(1, Letters1.Count), 1, 5);
            layer.Initialize();
            network.Layers[4] = layer;
            network.TagetOutputs = Letters1;

        }
        void CreateNetwork2()
        {
            network = new ConvolutionNetwork();
            //layer 0: inputlayer
            network.Layers = new NNLayer[6];
            network.LayerCount = 6;
            NNLayer layer = new NNLayer("00-Layer Input", null, new Size(29, 29), 1, 5);
            network.InputDesignedPatternSize = new Size(29, 29);
            layer.Initialize();
            network.Layers[0] = layer;
            layer = new NNLayer("01-Layer ConvolutionalSubsampling", layer, new Size(13, 13), 10, 5);
            layer.Initialize();
            network.Layers[1] = layer;
            layer = new NNLayer("02-Layer ConvolutionalSubsampling", layer, new Size(5, 5), 60, 5);
            layer.Initialize();
            network.Layers[2] = layer;
            layer = new NNLayer("03-Layer FullConnected", layer, new Size(1, 300), 1, 5);
            layer.Initialize();
            network.Layers[3] = layer;
            layer = new NNLayer("04-Layer FullConnected", layer, new Size(1, 200), 1, 5);
            layer.Initialize();
            network.Layers[4] = layer;
            layer = new NNLayer("05-Layer FullConnected", layer, new Size(1, Letters.Count), 1, 5);
            layer.Initialize();
            network.Layers[5] = layer;
            network.TagetOutputs = Letters;

        }
        private void btTrain_Click(object sender, EventArgs e)
        {
            if (isDatabaseReady && !isTrainingRuning)
            {
                TrainingParametersForm form = new TrainingParametersForm();
                form.Parameters = nnParameters;
                DialogResult result = form.ShowDialog();
                if (result == DialogResult.OK)
                {
                    nnParameters = form.Parameters;
                    ByteImageData[] dt = new ByteImageData[dataProvider.ByteImagePatterns.Count];
                    dataProvider.ByteImagePatterns.CopyTo(dt);
                    nnParameters.RealPatternSize = dataProvider.PatternSize;
                    if (network == null)
                    {
                        CreateNetwork(); //create network for training
                        NetworkInformation();
                    }
                    var ntraining = new Neurons.NNTrainPatterns(network, dt, nnParameters, true, this);
                    tokenSource = new CancellationTokenSource();
                    token = tokenSource.Token;
                    this.btTrain.Image = global::NNControl.Properties.Resources.Stop_sign;
                    this.btLoad.Enabled = false;
                    this.btnOpen.Enabled = false;
                    maintask = Task.Factory.StartNew(() =>
                    {
                        if (stopwatch.IsRunning)
                        {
                            // Stop the timer; show the start and reset buttons.
                            stopwatch.Stop();
                        }
                        else
                        {
                            // Start the timer; show the stop and lap buttons.
                            stopwatch.Reset();
                            stopwatch.Start();
                        }
                        isTrainingRuning = true;
                        ntraining.BackpropagationThread(token);
                        if (token.IsCancellationRequested)
                        {
                            String s = String.Format("BackPropagation is canceled");
                            this.Invoke(this.DelegateAddObject, new Object[] { 4, s });
                            token.ThrowIfCancellationRequested();
                        }
                    },token);
                   
                }
            }
            else
            {
                tokenSource.Cancel();
               
            }
            
        }
        void BackPropagationThreadsFinished()
        {
            if (isTrainingRuning)
            {
                var msResult = MessageBox.Show("Do you want to save Neural Network data ?", "Save Neural Network Data", MessageBoxButtons.OKCancel);
                if (msResult == DialogResult.OK)
                {
                    using (var saveFileDialog1 = new System.Windows.Forms.SaveFileDialog { Filter = "Neural network parameters file (*.nnt)|*.nnt", Title = "Save Neural network File" })
                    {
                        var rs = saveFileDialog1.ShowDialog();
                        if (rs == DialogResult.OK)
                        {

                            var fsIn = saveFileDialog1.OpenFile();
                            var arIn = new Archive(fsIn, ArchiveOp.store);
                            network.Serialize(arIn);
                            fsIn.Close();
                        }
                    }
                }
                isTrainingRuning = false;
                this.btTrain.Image = global::NNControl.Properties.Resources.btnNext;
                this.btLoad.Enabled = true;
                this.btnOpen.Enabled = true;
                if (stopwatch.IsRunning)
                {
                    // Stop the timer; show the start and reset buttons.
                    stopwatch.Stop();
                }
            }
            return;
        }
        private void btLoad_Click(object sender, EventArgs e)
        {
            using (var OpenFileDialog1 = new System.Windows.Forms.OpenFileDialog { Filter = "Neural network parameters file (*.nnt)|*.nnt", Title = "Load Neural network File" })
            {
                if (OpenFileDialog1.ShowDialog() == DialogResult.OK)
                {
                    network = new ConvolutionNetwork();
                    nntfile = OpenFileDialog1.FileName;
                    var fsIn = OpenFileDialog1.OpenFile();
                    var arIn = new Archive(fsIn, ArchiveOp.load);
                    network.Serialize(arIn);
                    fsIn.Close();
                    NetworkInformation();
                }
            }
        }
        public Bitmap CopyDataToBitmap(byte[] data,Size size)
        {
            //Here create the Bitmap to the know height, width and format
            
                Bitmap bmp = new Bitmap(size.Width, size.Height, PixelFormat.Format8bppIndexed);
                ColorPalette ncp = bmp.Palette;
                for (int i = 0; i < 256; i++)
                    ncp.Entries[i] = Color.FromArgb(255, i, i, i);
                bmp.Palette = ncp;
                     
                    //Create a BitmapData and Lock all pixels to be written 
                    BitmapData bmpData = bmp.LockBits(
                                            new Rectangle(0, 0, bmp.Width, bmp.Height),
                                            ImageLockMode.WriteOnly, bmp.PixelFormat);
                    int bytes = Math.Abs(bmpData.Stride) * bmp.Height;
                    byte[] rgbValues = new byte[bytes];
                    for (int i = 0; i < bytes;i++ )
                    {
                        rgbValues[i] = 255;
                    }
                    int bmpWidth = bmp.Width;
                    int bmpHeight = bmp.Height;
                    //
                    /*TODO: Check potentially-changing upper bound expression "gsBitmap.Height" which is now called only *once*,
                    to ensure the new Parallel.For call matches behavior in the original for-loop
                    (where this upper bound expression had previously been evaluated at the start of *every* loop iteration).*/
                    Parallel.For(0, bmpHeight, (h, loopstate) =>
                    {
                        for (int w = 0; w < bmpWidth; w++)
                        {
                            rgbValues[h * bmpData.Stride + w] = data[h * bmpWidth + w];
                        }

                    });
                    //Copy the data from the byte array into BitmapData.Scan0
                    System.Runtime.InteropServices.Marshal.Copy(rgbValues, 0, bmpData.Scan0, rgbValues.Length);
                    //Unlock the pixels
                    bmp.UnlockBits(bmpData);

           //Return the bitmap 

            return bmp;
        }
        private void NetworkInformation()
        {
            lvNetwork.Items.Clear();
            lvNetwork.Groups.Clear();
            int i=0;
            foreach (var layer in network.Layers)
            {
                String label = layer.Label;
                ListViewGroup lvgroup = new ListViewGroup(label);
                lvNetwork.Groups.Add(lvgroup);
                String [] itemTexts=new String[5];
                int neurons=layer.NeuronCount;
                itemTexts[0] = neurons.ToString();
                itemTexts[1] = layer.FeatureMapSize.ToString();
                itemTexts[2] = layer.FeatureMapCount.ToString();
                itemTexts[3] = layer.WeightCount.ToString();
                itemTexts[4] = (layer.NeuronCount * layer.Neurons[0].ConnectionCount).ToString();
                ListViewItem item = new ListViewItem(itemTexts);
                lvNetwork.Items.Add(item);
                item.Group = lvgroup;
            }
        }
        private void timerMain_Tick(object sender, EventArgs e)
        {
            if (stopwatch.IsRunning)
            {
                // Get the elapsed time as a TimeSpan value.
                TimeSpan ts = stopwatch.Elapsed;

                // Format and display the TimeSpan value.
                toolStripStatusLabel1.Text = String.Format("{0:00}:{1:00}:{2:00}.{3:00}",
                    ts.Hours, ts.Minutes, ts.Seconds,
                    ts.Milliseconds / 10);

                // If the user has just clicked the "Lap" button,
                // then capture the current time for the lap time.
            }
        }
    }
}

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About the Author

Vietdungiitb
Vietnam Maritime University
Vietnam Vietnam
No Biography provided

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