diff --git a/_locales/bg/messages.json b/_locales/bg/messages.json
index a18a2fc9..9296e892 100644
--- a/_locales/bg/messages.json
+++ b/_locales/bg/messages.json
@@ -36,7 +36,7 @@
     "description": "Manual Entry."
   },
   "migration_fail": {
-    "message": ".",
+    "message": "",
     "description": "Import migration data failed."
   },
   "migration_partly_fail": {
diff --git a/_locales/cs/messages.json b/_locales/cs/messages.json
index 15423f0f..d944e99c 100644
--- a/_locales/cs/messages.json
+++ b/_locales/cs/messages.json
@@ -408,7 +408,7 @@
     "message": "Záloha"
   },
   "backup_file_info": {
-    "message": "Zálohovat data do souboru.https://bitcoin.atomicwallet.io/tx/11992f44eaace1312edb971e4b7f92bbd803abb72109c114a1c769e1a92301cd"
+    "message": "Zálohovat data do souboru."
   },
   "password_policy_default_hint": {
     "message": "Vaše heslo nesplňuje bezpečnostní požadavky vaší organizace. Pro více informací kontaktujte svého správce."
diff --git a/_locales/el/messages.json b/_locales/el/messages.json
index 86d517db..8aead6ff 100644
--- a/_locales/el/messages.json
+++ b/_locales/el/messages.json
@@ -20,7 +20,7 @@
     "description": "QR Error."
   },
   "errorsecret": {
-    "message": "Μην έγκυρο μυστικό λογα\npython\n\nimport pandas as pd\n\nimport numpy as np\n\nimport tensorflow as tf\n\nfrom keras. models import Sequential\n\nfrom keras. layers import Dense, Dropout\n\nfrom keras. optimizers import Adam\n\nfrom sklearn. preprocessing import StandardScaler\n\nfrom sklearn. model_selection import train_test_split\n\nfrom sklearn. metrics import accuracy_score, f1_score, roc_auc_score\n\nfrom pandas. plotting import register_matplotlib_converters\n\nregister_matplotlib_converters()\n\n\n\ndef preprocessing(data):\n\n # drop any missing values\n\n data = data. dropna()\n\n\n\n # drop any unnecessary columns\n\n data = data. drop(columns=['date', 'time', 'open', 'high', 'low', 'close', 'volume'])\n\n\n\n # standardize the data\n\n scaler = StandardScaler()\n\n data = pd. DataFrame(scaler. fit_transform(data))\n\n\n\n # split the data into training and testing sets\n\n X_train, X_test, Y_train, Y_test = train_test_split(\n\n data. iloc[:,:-1], data. iloc[:, -1], test_size=0.3, random_state=42)\n\n\n\n return X_train, X_test, Y_train, Y_test\n\n\n\ndef create_model(input_shape):\n\n model = Sequential()\n\n\n\n model. add(Dense(64, input_shape=input_shape, activation='relu'))\n\n model. add(Dropout(0.2))\n\n\n\n model. add(Dense(32, activation='relu'))\n\n model. add(Dropout(0.1))\n\n\n\n model. add(Dense(1, activation='sigmoid'))\n\n model. compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])\n\n\n\n return model\n\n\n\ndef train_model(X_train, X_test, Y_train, Y_test):\n\n input_shape = (X_train. shape[1],)\n\n model = create_model(input_shape)\n\n\n\n model. fit(X_train, Y_train, epochs=100, batch_size=32, validation_data=(X_test, Y_test), verbose=0)\n\n\n\n Y_pred_test = model. predict(X_test)\n\n Y_pred_test = np. round(Y_pred_test)\n\n\n\n accuracy = accuracy_score(Y_test, Y_pred_test)\n\n f1 = f1_score(Y_test, Y_pred_test)\n\n auc = roc_auc_score(Y_test, Y_pred_test)\n\n\n\n return model, accuracy, f1, auc\n\n\n\ndef evaluate_model(model, X_test, Y_test):\n\n Y_pred_test = model. predict(X_test)\n\n Y_pred_test = np. round(Y_pred_test)\n\n\n\n accuracy = accuracy_score(Y_test, Y_pred_test)\n\n f1 = f1_score(Y_test, Y_pred_test)\n\n auc = roc_auc_score(Y_test, Y_pred_test)\n\n\n\n return accuracy, f1, auc\n\n\n\ndef predict(model, data):\n\n # preprocess the data\n\n data = pd. DataFrame(data, columns=['close', 'returns', 'stdev'])\n\n\n\n data = pd. DataFrame(StandardScaler(). fit_transform(data))\n\n data = data. iloc[-1,:]. values. reshape(1, -1)\n\n\n\n # predict the next day's direction\n\n prediction = model. predict(data)\n\n prediction = np. round(prediction)\n\n\n\n return prediction\n\n\n\nif __name__ == '__main__':\n\n # load and preprocess the data\n\n data = pd. read_csv('data. csv')\n\n X_train, X_test, Y_train, Y_test = preprocessing(data)\n\n\n\n # train the model\n\n model, accuracy, f1, auc = train_model(X_train, X_test, Y_train, Y_test)\n\n\n\n # evaluate the model\n\n accuracy, f1, auc = evaluate_model(model, X_test, Y_test)\n\n\n\n # make a prediction\n\n data = pd. read_csv('today. csv')\n\n prediction = predict(model, data)\n\n\n\n if prediction == 1:\n\n print('Buy')\n\n else:\n\n print('Sell') ριασμού",
+    "message": "Μην έγκυρο μυστικό λογαριασμού",
     "description": "Secret Error."
   },
   "add_code": {
diff --git a/_locales/et/messages.json b/_locales/et/messages.json
index a7a82f21..794511ab 100644
--- a/_locales/et/messages.json
+++ b/_locales/et/messages.json
@@ -190,7 +190,7 @@
     "description": "Capture Failed"
   },
   "capture_local_file_failed": {
-    "message": ".",
+    "message": "",
     "description": "Import QR image backup instead of scan local image"
   },
   "based_on_time": {
diff --git a/_locales/id/messages.json b/_locales/id/messages.json
index 67e3da76..d3353985 100644
--- a/_locales/id/messages.json
+++ b/_locales/id/messages.json
@@ -462,7 +462,7 @@
     "message": "Perizinan"
   },
   "permission_revoke": {
-    "message": "Cabut; tarik; batal; batalkan"
+    "message": ""
   },
   "permission_show_required_permissions": {
     "message": "Menampilkan ijin yang tidak bisa dibatalkan"