diff --git a/.eslintrc b/.eslintrc
index e1c40a0adb..88af3ecf32 100644
--- a/.eslintrc
+++ b/.eslintrc
@@ -12,9 +12,10 @@
     "arrow-body-style": "off",
     "no-loop-func": "off"
   },
+  "ignorePatterns": ["*.md", "*.png", "*.jpeg", "*.jpg"],
   "settings": {
     "react": {
-      "version": "latest"
+      "version": "18.2.0"
     }
   }
 }
diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 912c719df3..e007b6c910 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        node-version: [ 14.x ]
+        node-version: [ 16.x ]
 
     steps:
       - name: Checkout repository
diff --git a/.nvmrc b/.nvmrc
index 958b5a36e1..7fd023741b 100644
--- a/.nvmrc
+++ b/.nvmrc
@@ -1 +1 @@
-v14
+v16.15.0
diff --git a/BACKERS.md b/BACKERS.md
index 62c397a38c..a244d3bed3 100644
--- a/BACKERS.md
+++ b/BACKERS.md
@@ -14,6 +14,24 @@
 
 `null`
 
+<!--
+<table>
+  <tr>
+    <td align="center">
+      <a href="[PROFILE_URL]">
+        <img
+          src="[PROFILE_IMG_SRC]"
+          width="50"
+          height="50"
+        />
+      </a>
+      <br />
+      <a href="[PROFILE_URL]">[PROFILE_NAME]</a>
+    </td>
+  </tr>
+</table>
+-->
+
 <!--
 <ul>
   <li>
diff --git a/README.ar-AR.md b/README.ar-AR.md
index cbcfd02409..2ff0baddbd 100644
--- a/README.ar-AR.md
+++ b/README.ar-AR.md
@@ -3,7 +3,7 @@
 [![Build Status](https://travis-ci.org/trekhleb/javascript-algorithms.svg?branch=master)](https://travis-ci.org/trekhleb/javascript-algorithms)
 [![codecov](https://codecov.io/gh/trekhleb/javascript-algorithms/branch/master/graph/badge.svg)](https://codecov.io/gh/trekhleb/javascript-algorithms)
 
-تحتوي هذا مقالة على أمثلة عديدة تستند إلى الخوارزميات الشائعة وهياكل البيانات في الجافا سكريبت.
+تحتوي هذه المقالة على أمثلة عديدة تستند إلى الخوارزميات الشائعة وهياكل البيانات في الجافا سكريبت.
 
 كل خوارزمية وهياكل البيانات لها برنامج README منفصل خاص بها
 مع التفسيرات والروابط ذات الصلة لمزيد من القراءة (بما في ذلك تلك
@@ -23,7 +23,9 @@ _اقرأ هذا في لغات أخرى:_
 [_Türk_](README.tr-TR.md),
 [_Italiana_](README.it-IT.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
  ☝ ملاحضة هذا المشروع مخصص للاستخدام لأغراض التعلم والبحث
 فقط ، و ** ليست ** معدة للاستخدام في **الإنتاج**
diff --git a/README.de-DE.md b/README.de-DE.md
index b34ad3563f..019e7d6b39 100644
--- a/README.de-DE.md
+++ b/README.de-DE.md
@@ -24,7 +24,9 @@ _Lies dies in anderen Sprachen:_
 [_Italiana_](README.it-IT.md),
 [_Bahasa Indonesia_](README.id-ID.md),
 [_Українська_](README.uk-UA.md),
-[_Arabic_](README.ar-AR.md)
+[_Arabic_](README.ar-AR.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 _☝ Beachte, dass dieses Projekt nur für Lern- und Forschungszwecke gedacht ist und **nicht** für den produktiven Einsatz verwendet werden soll_
 
diff --git a/README.es-ES.md b/README.es-ES.md
index d79baf6022..3a15ada4bd 100644
--- a/README.es-ES.md
+++ b/README.es-ES.md
@@ -25,7 +25,9 @@ _Léelo en otros idiomas:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Nótese que este proyecto está pensado con fines de aprendizaje e investigación,
 y **no** para ser usado en producción.*
@@ -69,7 +71,7 @@ definen con precisión una secuencia de operaciones.
 * **Matemáticas**
   * `P` [Manipulación de bits](src/algorithms/math/bits) - asignar/obtener/actualizar/limpiar bits, multiplicación/división por dos, hacer negativo, etc.
   * `P` [Factorial](src/algorithms/math/factorial)
-  * `P` [Número de Fibonacci](src/algorithms/math/fibonacci)
+  * `P` [Sucesión de Fibonacci](src/algorithms/math/fibonacci)
   * `P` [Prueba de primalidad](src/algorithms/math/primality-test) (método de división de prueba)
   * `P` [Algoritmo de Euclides](src/algorithms/math/euclidean-algorithm) - calcular el Máximo común divisor (MCD)
   * `P` [Mínimo común múltiplo](src/algorithms/math/least-common-multiple) (MCM)
diff --git a/README.fr-FR.md b/README.fr-FR.md
index 469a4f52d9..cb30d1833d 100644
--- a/README.fr-FR.md
+++ b/README.fr-FR.md
@@ -26,7 +26,9 @@ _Lisez ceci dans d'autres langues:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## Data Structures
 
diff --git a/README.he-IL.md b/README.he-IL.md
new file mode 100644
index 0000000000..43ef196e60
--- /dev/null
+++ b/README.he-IL.md
@@ -0,0 +1,370 @@
+# אלגוריתמים ומבני נתונים ב-JavaScript
+
+[![CI](https://github.com/trekhleb/javascript-algorithms/workflows/CI/badge.svg)](https://github.com/trekhleb/javascript-algorithms/actions?query=workflow%3ACI+branch%3Amaster)
+[![codecov](https://codecov.io/gh/trekhleb/javascript-algorithms/branch/master/graph/badge.svg)](https://codecov.io/gh/trekhleb/javascript-algorithms)
+![גודל המאגר](https://img.shields.io/github/repo-size/trekhleb/javascript-algorithms.svg)
+
+מאגר זה מכיל דוגמאות מבוססות JavaScript של אלגוריתמים ומבני נתונים פופולריים רבים.
+
+לכל אלגוריתם ומבנה נתונים יש README משלו עם הסברים קשורים וקישורים לקריאה נוספת (כולל קישורים לסרטוני YouTube).
+
+_קרא זאת בשפות אחרות:_
+[_简体中文_](README.zh-CN.md),
+[_繁體中文_](README.zh-TW.md),
+[_한국어_](README.ko-KR.md),
+[_日本語_](README.ja-JP.md),
+[_Polski_](README.pl-PL.md),
+[_Français_](README.fr-FR.md),
+[_Español_](README.es-ES.md),
+[_Português_](README.pt-BR.md),
+[_Русский_](README.ru-RU.md),
+[_Türkçe_](README.tr-TR.md),
+[_Italiana_](README.it-IT.md),
+[_Bahasa Indonesia_](README.id-ID.md),
+[_Українська_](README.uk-UA.md),
+[_Arabic_](README.ar-AR.md),
+[_Tiếng Việt_](README.vi-VN.md),
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+
+*☝ שים לב שפרויקט זה מיועד למטרות לימוד ומחקר בלבד, ואינו מיועד לשימוש בייצור.*
+
+## מבני נתונים
+
+מבנה נתונים הוא דרך מסוימת לארגן ולאחסן נתונים במחשב כך שניתן לגשת אליהם ולשנות אותם ביעילות. ליתר דיוק, מבנה נתונים הוא אוסף של ערכי נתונים, היחסים ביניהם, והפונקציות או הפעולות שניתן ליישם על הנתונים.
+
+זכור שלכל מבנה נתונים יש את היתרונות והחסרונות שלו. חשוב לשים לב יותר לסיבה שבגללה אתה בוחר מבנה נתונים מסוים מאשר לאופן היישום שלו.
+
+`B` - מתחיל, `A` - מתקדם
+
+* `B` [רשימה מקושרת](src/data-structures/linked-list)
+* `B` [רשימה מקושרת כפולה](src/data-structures/doubly-linked-list)
+* `B` [תור](src/data-structures/queue)
+* `B` [מחסנית](src/data-structures/stack)
+* `B` [טבלת גיבוב](src/data-structures/hash-table)
+* `B` [ערימה](src/data-structures/heap) - גרסאות מקסימום ומינימום
+* `B` [תור עדיפויות](src/data-structures/priority-queue)
+* `A` [עץ תחיליות](src/data-structures/trie)
+* `A` [עץ](src/data-structures/tree)
+  * `A` [עץ חיפוש בינארי](src/data-structures/tree/binary-search-tree)
+  * `A` [עץ AVL](src/data-structures/tree/avl-tree)
+  * `A` [עץ אדום-שחור](src/data-structures/tree/red-black-tree)
+  * `A` [עץ מקטעים](src/data-structures/tree/segment-tree) - עם דוגמאות לשאילתות מינימום/מקסימום/סכום של טווח
+  * `A` [עץ פנוויק](src/data-structures/tree/fenwick-tree) (עץ בינארי מאונדקס)
+* `A` [גרף](src/data-structures/graph) (מכוון ולא מכוון)
+* `A` [קבוצה מופרדת](src/data-structures/disjoint-set) - מבנה נתונים של איחוד-מציאה או מיזוג-מציאה
+* `A` [מסנן בלום](src/data-structures/bloom-filter)
+* `A` [מטמון LRU](src/data-structures/lru-cache/) - מטמון פחות שימוש לאחרונה (LRU)
+
+## אלגוריתמים
+
+אלגוריתם הוא מפרט חד משמעי כיצד לפתור סוג של בעיות. זוהי קבוצה של כללים המגדירים במדויק רצף של פעולות.
+
+`B` - מתחיל, `A` - מתקדם
+
+### אלגוריתמים לפי נושא
+
+* **מתמטיקה**
+  * `B` [מניפולציה על ביטים](src/algorithms/math/bits) - קביעה/עדכון/ניקוי ביטים, הכפלה/חילוק ב-2, הפיכה לשלילי וכו'
+  * `B` [נקודה צפה בינארית](src/algorithms/math/binary-floating-point) - ייצוג בינארי של מספרים בנקודה צפה
+  * `B` [פקטוריאל](src/algorithms/math/factorial)
+  * `B` [מספר פיבונאצ'י](src/algorithms/math/fibonacci) - גרסאות קלאסיות וסגורות
+  * `B` [גורמים ראשוניים](src/algorithms/math/prime-factors) - מציאת גורמים ראשוניים וספירתם באמצעות משפט הארדי-רמנוג'אן
+  * `B` [בדיקת ראשוניות](src/algorithms/math/primality-test) (שיטת החלוקה הניסיונית)
+  * `B` [אלגוריתם אוקלידס](src/algorithms/math/euclidean-algorithm) - חישוב המחלק המשותף הגדול ביותר (GCD)
+  * `B` [המכפיל המשותף הקטן ביותר](src/algorithms/math/least-common-multiple) (LCM)
+  * `B` [נפה של ארטוסתנס](src/algorithms/math/sieve-of-eratosthenes) - מציאת כל המספרים הראשוניים עד לגבול כלשהו
+  * `B` [האם חזקה של שתיים](src/algorithms/math/is-power-of-two) - בדיקה אם מספר הוא חזקה של שתיים (אלגוריתמים נאיביים וביטיים)
+  * `B` [משולש פסקל](src/algorithms/math/pascal-triangle)
+  * `B` [מספר מרוכב](src/algorithms/math/complex-number) - מספרים מרוכבים ופעולות בסיסיות עליהם
+  * `B` [רדיאן ומעלות](src/algorithms/math/radian) - המרה מרדיאנים למעלות ובחזרה
+  * `B` [חזקה מהירה](src/algorithms/math/fast-powering)
+  * `B` [שיטת הורנר](src/algorithms/math/horner-method) - הערכת פולינום
+  * `B` [מטריצות](src/algorithms/math/matrix) - מטריצות ופעולות בסיסיות על מטריצות (כפל, טרנספוזיציה וכו')
+  * `B` [מרחק אוקלידי](src/algorithms/math/euclidean-distance) - מרחק בין שתי נקודות/וקטורים/מטריצות
+  * `A` [חלוקת מספר שלם](src/algorithms/math/integer-partition)
+  * `A` [שורש ריבועי](src/algorithms/math/square-root) - שיטת ניוטון
+  * `A` [אלגוריתם π של ליו הוי](src/algorithms/math/liu-hui) - חישובי π מקורבים על בסיס N-גונים
+  * `A` [התמרת פורייה הבדידה](src/algorithms/math/fourier-transform) - פירוק פונקציה של זמן (אות) לתדרים המרכיבים אותה
+
+* **קבוצות**
+  * `B` [מכפלה קרטזית](src/algorithms/sets/cartesian-product) - מכפלה של מספר קבוצות
+  * `B` [ערבוב פישר-ייטס](src/algorithms/sets/fisher-yates) - תמורה אקראית של רצף סופי
+  * `A` [קבוצת חזקה](src/algorithms/sets/power-set) - כל תתי הקבוצות של קבוצה (פתרונות ביטיים, מעקב לאחור וקסקדה)
+  * `A` [תמורות](src/algorithms/sets/permutations) (עם ובלי חזרות)
+  * `A` [צירופים](src/algorithms/sets/combinations) (עם ובלי חזרות)
+  * `A` [תת-רצף משותף ארוך ביותר](src/algorithms/sets/longest-common-subsequence) (LCS)
+  * `A` [תת-רצף עולה ארוך ביותר](src/algorithms/sets/longest-increasing-subsequence)
+  * `A` [על-רצף משותף קצר ביותר](src/algorithms/sets/shortest-common-supersequence) (SCS)
+  * `A` [בעיית התרמיל](src/algorithms/sets/knapsack-problem) - "0/1" ו"לא מוגבל"
+  * `A` [תת-מערך מקסימלי](src/algorithms/sets/maximum-subarray) - "כוח ברוטלי" ו"תכנות דינמי" (Kadane) גרסאות
+  * `A` [סכום צירוף](src/algorithms/sets/combination-sum) - מציאת כל הצירופים שיוצרים סכום ספציפי
+
+* **מחרוזות**
+  * `B` [מרחק המינג](src/algorithms/string/hamming-distance) - מספר העמדות שבהן הסמלים שונים
+  * `B` [פלינדרום](src/algorithms/string/palindrome) - בדיקה אם המחרוזת זהה בקריאה לאחור
+  * `A` [מרחק לוונשטיין](src/algorithms/string/levenshtein-distance) - מרחק העריכה המינימלי בין שתי רצפים
+  * `A` [אלגוריתם קנות'-מוריס-פראט](src/algorithms/string/knuth-morris-pratt) (אלגוריתם KMP) - חיפוש תת-מחרוזת (התאמת תבנית)
+  * `A` [אלגוריתם Z](src/algorithms/string/z-algorithm) - חיפוש תת-מחרוזת (התאמת תבנית)
+  * `A` [אלגוריתם רבין קארפ](src/algorithms/string/rabin-karp) - חיפוש תת-מחרוזת
+  * `A` [תת-מחרוזת משותפת ארוכה ביותר](src/algorithms/string/longest-common-substring)
+  * `A` [התאמת ביטוי רגולרי](src/algorithms/string/regular-expression-matching)
+
+* **חיפושים**
+  * `B` [חיפוש לינארי](src/algorithms/search/linear-search)
+  * `B` [חיפוש קפיצות](src/algorithms/search/jump-search) (או חיפוש בלוקים) - חיפוש במערך ממוין
+  * `B` [חיפוש בינארי](src/algorithms/search/binary-search) - חיפוש במערך ממוין
+  * `B` [חיפוש אינטרפולציה](src/algorithms/search/interpolation-search) - חיפוש במערך ממוין עם התפלגות אחידה
+
+* **מיון**
+  * `B` [מיון בועות](src/algorithms/sorting/bubble-sort)
+  * `B` [מיון בחירה](src/algorithms/sorting/selection-sort)
+  * `B` [מיון הכנסה](src/algorithms/sorting/insertion-sort)
+  * `B` [מיון ערימה](src/algorithms/sorting/heap-sort)
+  * `B` [מיון מיזוג](src/algorithms/sorting/merge-sort)
+  * `B` [מיון מהיר](src/algorithms/sorting/quick-sort) - יישומים במקום ולא במקום
+  * `B` [מיון צדפות](src/algorithms/sorting/shell-sort)
+  * `B` [מיון ספירה](src/algorithms/sorting/counting-sort)
+  * `B` [מיון בסיס](src/algorithms/sorting/radix-sort)
+  * `B` [מיון דלי](src/algorithms/sorting/bucket-sort)
+
+* **רשימות מקושרות**
+  * `B` [מעבר ישר](src/algorithms/linked-list/traversal)
+  * `B` [מעבר הפוך](src/algorithms/linked-list/reverse-traversal)
+
+* **עצים**
+  * `B` [חיפוש לעומק](src/algorithms/tree/depth-first-search) (DFS)
+  * `B` [חיפוש לרוחב](src/algorithms/tree/breadth-first-search) (BFS)
+
+* **גרפים**
+  * `B` [חיפוש לעומק](src/algorithms/graph/depth-first-search) (DFS)
+  * `B` [חיפוש לרוחב](src/algorithms/graph/breadth-first-search) (BFS)
+  * `B` [אלגוריתם קרוסקל](src/algorithms/graph/kruskal) - מציאת עץ פורש מינימלי (MST) עבור גרף לא מכוון משוקלל
+  * `A` [אלגוריתם דייקסטרה](src/algorithms/graph/dijkstra) - מציאת המסלולים הקצרים ביותר לכל קודקודי הגרף מקודקוד יחיד
+  * `A` [אלגוריתם בלמן-פורד](src/algorithms/graph/bellman-ford) - מציאת המסלולים הקצרים ביותר לכל קודקודי הגרף מקודקוד יחיד
+  * `A` [אלגוריתם פלויד-וורשל](src/algorithms/graph/floyd-warshall) - מציאת המסלולים הקצרים ביותר בין כל זוגות הקודקודים
+  * `A` [זיהוי מעגל](src/algorithms/graph/detect-cycle) - עבור גרפים מכוונים ולא מכוונים (גרסאות מבוססות DFS וקבוצה מופרדת)
+  * `A` [אלגוריתם פרים](src/algorithms/graph/prim) - מציאת עץ פורש מינימלי (MST) עבור גרף לא מכוון משוקלל
+  * `A` [מיון טופולוגי](src/algorithms/graph/topological-sorting) - שיטת DFS
+  * `A` [נקודות חיתוך](src/algorithms/graph/articulation-points) - אלגוריתם טרג'ן (מבוסס DFS)
+  * `A` [גשרים](src/algorithms/graph/bridges) - אלגוריתם מבוסס DFS
+  * `A` [מסלול ומעגל אוילר](src/algorithms/graph/eulerian-path) - אלגוריתם פלרי - ביקור בכל קשת בדיוק פעם אחת
+  * `A` [מעגל המילטון](src/algorithms/graph/hamiltonian-cycle) - ביקור בכל קודקוד בדיוק פעם אחת
+  * `A` [רכיבים קשירים חזק](src/algorithms/graph/strongly-connected-components) - אלגוריתם קוסרג'ו
+  * `A` [בעיית הסוכן הנוסע](src/algorithms/graph/travelling-salesman) - המסלול הקצר ביותר האפשרי שמבקר בכל עיר וחוזר לעיר המוצא
+
+* **הצפנה**
+  * `B` [גיבוב פולינומי](src/algorithms/cryptography/polynomial-hash) - פונקציית גיבוב מתגלגלת המבוססת על פולינום
+  * `B` [צופן גדר מסילה](src/algorithms/cryptography/rail-fence-cipher) - אלגוריתם הצפנת טרנספוזיציה להצפנת הודעות
+  * `B` [צופן קיסר](src/algorithms/cryptography/caesar-cipher) - צופן החלפה פשוט
+  * `B` [צופן היל](src/algorithms/cryptography/hill-cipher) - צופן החלפה המבוסס על אלגברה לינארית
+
+* **למידת מכונה**
+  * `B` [NanoNeuron](https://github.com/trekhleb/nano-neuron) - 7 פונקציות JS פשוטות שמדגימות כיצד מכונות יכולות ללמוד באמת (תפוצה קדימה/אחורה)
+  * `B` [k-NN](src/algorithms/ml/knn) - אלגוריתם סיווג k-השכנים הקרובים ביותר
+  * `B` [k-Means](src/algorithms/ml/k-means) - אלגוריתם אשכול k-Means
+
+* **עיבוד תמונה**
+  * `B` [Seam Carving](src/algorithms/image-processing/seam-carving) - אלגוריתם שינוי גודל תמונה מודע תוכן
+
+* **סטטיסטיקה**
+  * `B` [משקל אקראי](src/algorithms/statistics/weighted-random) - בחירת פריט אקראי מהרשימה על בסיס משקלי הפריטים
+
+* **אלגוריתמים אבולוציוניים**
+  * `A` [אלגוריתם גנטי](https://github.com/trekhleb/self-parking-car-evolution) - דוגמה לאופן שבו ניתן ליישם אלגוריתם גנטי לאימון מכוניות בחניה עצמית
+
+* **לא מסווג**
+  * `B` [מגדלי האנוי](src/algorithms/uncategorized/hanoi-tower)
+  * `B` [סיבוב מטריצה ריבועית](src/algorithms/uncategorized/square-matrix-rotation) - אלגוריתם במקום
+  * `B` [משחק הקפיצות](src/algorithms/uncategorized/jump-game) - דוגמאות למעקב לאחור, תכנות דינמי (מלמעלה למטה + מלמטה למעלה) וחמדני
+  * `B` [מסלולים ייחודיים](src/algorithms/uncategorized/unique-paths) - דוגמאות למעקב לאחור, תכנות דינמי ומבוססות על משולש פסקל
+  * `B` [מדרגות גשם](src/algorithms/uncategorized/rain-terraces) - בעיית לכידת מי גשם (גרסאות תכנות דינמי וכוח ברוטלי)
+  * `B` [מדרגות רקורסיביות](src/algorithms/uncategorized/recursive-staircase) - ספירת מספר הדרכים להגיע לראש (4 פתרונות)
+  * `B` [הזמן הטוב ביותר לקנות ולמכור מניות](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - דוגמאות לחלוקה וכיבוש ומעבר אחד
+  * `A` [בעיית N-המלכות](src/algorithms/uncategorized/n-queens)
+  * `A` [סיור הפרש](src/algorithms/uncategorized/knight-tour)
+
+### אלגוריתמים לפי פרדיגמה
+
+פרדיגמה אלגוריתמית היא שיטה או גישה כללית המונחת בבסיס התכנון של מחלקת אלגוריתמים. זוהי הפשטה גבוהה יותר מהמושג של אלגוריתם, בדיוק כפי שאלגוריתם הוא הפשטה גבוהה יותר מתוכנית מחשב.
+
+* **כוח ברוטלי** - בודק את כל האפשרויות ובוחר את הפתרון הטוב ביותר
+  * `B` [חיפוש לינארי](src/algorithms/search/linear-search)
+  * `B` [מדרגות גשם](src/algorithms/uncategorized/rain-terraces) - בעיית לכידת מי גשם
+  * `B` [מדרגות רקורסיביות](src/algorithms/uncategorized/recursive-staircase) - ספירת מספר הדרכים להגיע לראש
+  * `A` [תת-מערך מקסימלי](src/algorithms/sets/maximum-subarray)
+  * `A` [בעיית הסוכן הנוסע](src/algorithms/graph/travelling-salesman) - המסלול הקצר ביותר האפשרי שמבקר בכל עיר וחוזר לעיר המוצא
+  * `A` [התמרת פורייה הבדידה](src/algorithms/math/fourier-transform) - פירוק פונקציה של זמן (אות) לתדרים המרכיבים אותה
+
+* **חמדני** - בוחר את האפשרות הטובה ביותר בזמן הנוכחי, ללא כל התחשבות בעתיד
+  * `B` [משחק הקפיצות](src/algorithms/uncategorized/jump-game)
+  * `A` [בעיית התרמיל הלא מוגבל](src/algorithms/sets/knapsack-problem)
+  * `A` [אלגוריתם דייקסטרה](src/algorithms/graph/dijkstra) - מציאת המסלולים הקצרים ביותר לכל קודקודי הגרף
+  * `A` [אלגוריתם פרים](src/algorithms/graph/prim) - מציאת עץ פורש מינימלי (MST) עבור גרף לא מכוון משוקלל
+  * `A` [אלגוריתם קרוסקל](src/algorithms/graph/kruskal) - מציאת עץ פורש מינימלי (MST) עבור גרף לא מכוון משוקלל
+
+* **חלוקה וכיבוש** - מחלק את הבעיה לחלקים קטנים יותר ואז פותר חלקים אלה
+  * `B` [חיפוש בינארי](src/algorithms/search/binary-search)
+  * `B` [מגדלי האנוי](src/algorithms/uncategorized/hanoi-tower)
+  * `B` [משולש פסקל](src/algorithms/math/pascal-triangle)
+  * `B` [אלגוריתם אוקלידס](src/algorithms/math/euclidean-algorithm) - חישוב המחלק המשותף הגדול ביותר (GCD)
+  * `B` [מיון מיזוג](src/algorithms/sorting/merge-sort)
+  * `B` [מיון מהיר](src/algorithms/sorting/quick-sort)
+  * `B` [חיפוש לעומק בעץ](src/algorithms/tree/depth-first-search) (DFS)
+  * `B` [חיפוש לעומק בגרף](src/algorithms/graph/depth-first-search) (DFS)
+  * `B` [מטריצות](src/algorithms/math/matrix) - יצירה ומעבר על מטריצות בצורות שונות
+  * `B` [משחק הקפיצות](src/algorithms/uncategorized/jump-game)
+  * `B` [חזקה מהירה](src/algorithms/math/fast-powering)
+  * `B` [הזמן הטוב ביותר לקנות ולמכור מניות](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - דוגמאות לחלוקה וכיבוש ומעבר אחד
+  * `A` [תמורות](src/algorithms/sets/permutations) (עם ובלי חזרות)
+  * `A` [צירופים](src/algorithms/sets/combinations) (עם ובלי חזרות)
+  * `A` [תת-מערך מקסימלי](src/algorithms/sets/maximum-subarray)
+
+* **תכנות דינמי** - בניית פתרון באמצעות תת-פתרונות שנמצאו קודם לכן
+  * `B` [מספר פיבונאצ'י](src/algorithms/math/fibonacci)
+  * `B` [משחק הקפיצות](src/algorithms/uncategorized/jump-game)
+  * `B` [מסלולים ייחודיים](src/algorithms/uncategorized/unique-paths)
+  * `B` [מדרגות גשם](src/algorithms/uncategorized/rain-terraces) - בעיית לכידת מי גשם
+  * `B` [מדרגות רקורסיביות](src/algorithms/uncategorized/recursive-staircase) - ספירת מספר הדרכים להגיע לראש
+  * `B` [Seam Carving](src/algorithms/image-processing/seam-carving) - אלגוריתם שינוי גודל תמונה מודע תוכן
+  * `A` [מרחק לוונשטיין](src/algorithms/string/levenshtein-distance) - מרחק העריכה המינימלי בין שתי רצפים
+  * `A` [תת-רצף משותף ארוך ביותר](src/algorithms/sets/longest-common-subsequence) (LCS)
+  * `A` [תת-מחרוזת משותפת ארוכה ביותר](src/algorithms/string/longest-common-substring)
+  * `A` [תת-רצף עולה ארוך ביותר](src/algorithms/sets/longest-increasing-subsequence)
+  * `A` [על-רצף משותף קצר ביותר](src/algorithms/sets/shortest-common-supersequence)
+  * `A` [בעיית התרמיל 0/1](src/algorithms/sets/knapsack-problem)
+  * `A` [חלוקת מספר שלם](src/algorithms/math/integer-partition)
+  * `A` [תת-מערך מקסימלי](src/algorithms/sets/maximum-subarray)
+  * `A` [אלגוריתם בלמן-פורד](src/algorithms/graph/bellman-ford) - מציאת המסלולים הקצרים ביותר לכל קודקודי הגרף
+  * `A` [אלגוריתם פלויד-וורשל](src/algorithms/graph/floyd-warshall) - מציאת המסלולים הקצרים ביותר בין כל זוגות הקודקודים
+  * `A` [התאמת ביטוי רגולרי](src/algorithms/string/regular-expression-matching)
+
+* **מעקב לאחור** - בדומה לכוח ברוטלי, מנסה לייצר את כל הפתרונות האפשריים, אך בכל פעם שאתה מייצר פתרון הבא אתה בודק אם הוא עומד בכל התנאים, ורק אז ממשיך לייצר פתרונות הבאים. אחרת, חוזר אחורה, והולך בנתיב אחר של מציאת פתרון. בדרך כלל מעבר DFS של מרחב המצבים משמש.
+  * `B` [משחק הקפיצות](src/algorithms/uncategorized/jump-game)
+  * `B` [מסלולים ייחודיים](src/algorithms/uncategorized/unique-paths)
+  * `B` [קבוצת חזקה](src/algorithms/sets/power-set) - כל תתי הקבוצות של קבוצה
+  * `A` [מעגל המילטון](src/algorithms/graph/hamiltonian-cycle) - ביקור בכל קודקוד בדיוק פעם אחת
+  * `A` [בעיית N-המלכות](src/algorithms/uncategorized/n-queens)
+  * `A` [סיור הפרש](src/algorithms/uncategorized/knight-tour)
+  * `A` [סכום צירוף](src/algorithms/sets/combination-sum) - מציאת כל הצירופים שיוצרים סכום ספציפי
+
+* **סניף וחסום** - זוכר את הפתרון בעלות הנמוכה ביותר שנמצא בכל שלב של החיפוש המעקב לאחור, ומשתמש בעלות של הפתרון בעלות הנמוכה ביותר שנמצא עד כה כגבול תחתון על העלות של פתרון בעלות מינימלית לבעיה, על מנת לפסול פתרונות חלקיים עם עלויות גדולות יותר מהפתרון בעלות הנמוכה ביותר שנמצא עד כה. בדרך כלל מעבר BFS בשילוב עם מעבר DFS של עץ מרחב המצבים משמש.
+
+## כיצד להשתמש במאגר זה
+
+**התקנת כל התלויות**
+
+```
+npm install
+```
+
+**הרצת ESLint**
+
+ייתכן שתרצה להריץ אותו כדי לבדוק את איכות הקוד.
+
+```
+npm run lint
+```
+
+**הרצת כל הבדיקות**
+
+```
+npm test
+```
+
+**הרצת בדיקות לפי שם**
+
+```
+npm test -- 'LinkedList'
+```
+
+**פתרון בעיות**
+
+אם הלינטינג או הבדיקות נכשלים, נסה למחוק את התיקייה `node_modules` ולהתקין מחדש את חבילות npm:
+
+```
+rm -rf ./node_modules
+npm i
+```
+
+בנוסף, ודא שאתה משתמש בגרסת Node נכונה (`>=16`). אם אתה משתמש ב-[nvm](https://github.com/nvm-sh/nvm) לניהול גרסאות Node, תוכל להריץ `nvm use` מתיקיית השורש של הפרויקט והגרסה הנכונה תיבחר.
+
+**שטח משחקים**
+
+אתה יכול לשחק עם מבני נתונים ואלגוריתמים בקובץ `./src/playground/playground.js` ולכתוב
+בדיקות עבורו ב-`./src/playground/__test__/playground.test.js`.
+
+לאחר מכן פשוט הרץ את הפקודה הבאה כדי לבדוק אם קוד שטח המשחקים שלך עובד כמצופה:
+
+```
+npm test -- 'playground'
+```
+
+## מידע שימושי
+
+### הפניות
+
+- [▶ מבני נתונים ואלגוריתמים ב-YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [✍🏻 סקיצות של מבני נתונים](https://okso.app/showcase/data-structures)
+
+### סימון ה-O הגדול
+
+סימון *ה-O הגדול* משמש לסיווג אלגוריתמים לפי כיצד זמן הריצה או דרישות המרחב שלהם גדלים ככל שגודל הקלט גדל.
+בתרשים שלהלן תוכל למצוא את הסדרים הנפוצים ביותר של צמיחת אלגוריתמים המצוינים בסימון ה-O הגדול.
+
+![גרפי ה-O הגדול](./assets/big-o-graph.png)
+
+מקור: [Big O Cheat Sheet](http://bigocheatsheet.com/).
+
+להלן רשימה של כמה מסימוני ה-O הגדול הנפוצים ביותר והשוואות הביצועים שלהם מול גדלים שונים של נתוני קלט.
+
+| סימון ה-O הגדול | חישובים ל-10 אלמנטים | חישובים ל-100 אלמנטים | חישובים ל-1000 אלמנטים |
+| ---------------- | --------------------- | ---------------------- | ----------------------- |
+| **O(1)**         | 1                     | 1                      | 1                       |
+| **O(log N)**     | 3                     | 6                      | 9                       |
+| **O(N)**         | 10                    | 100                    | 1000                    |
+| **O(N log N)**   | 30                    | 600                    | 9000                    |
+| **O(N^2)**       | 100                   | 10000                  | 1000000                 |
+| **O(2^N)**       | 1024                  | 1.26e+29               | 1.07e+301               |
+| **O(N!)**        | 3628800               | 9.3e+157               | 4.02e+2567              |
+
+### מורכבות פעולות מבני נתונים
+
+| מבנה נתונים          | גישה    | חיפוש   | הכנסה   | מחיקה   | הערות  |
+| --------------------- | :-----: | :-----: | :-----: | :-----: | :------ |
+| **מערך**              | 1       | n       | n       | n       |         |
+| **מחסנית**            | n       | n       | 1       | 1       |         |
+| **תור**               | n       | n       | 1       | 1       |         |
+| **רשימה מקושרת**      | n       | n       | 1       | n       |         |
+| **טבלת גיבוב**        | -       | n       | n       | n       | במקרה של פונקציית גיבוב מושלמת, העלויות יהיו O(1) |
+| **עץ חיפוש בינארי**   | n       | n       | n       | n       | במקרה של עץ מאוזן, העלויות יהיו O(log(n)) |
+| **עץ B**              | log(n)  | log(n)  | log(n)  | log(n)  |         |
+| **עץ אדום-שחור**      | log(n)  | log(n)  | log(n)  | log(n)  |         |
+| **עץ AVL**            | log(n)  | log(n)  | log(n)  | log(n)  |         |
+| **מסנן בלום**         | -       | 1       | 1       | -       | תוצאות חיוביות שגויות אפשריות בעת חיפוש |
+
+### מורכבות אלגוריתמי מיון מערכים
+
+| שם                  | הטוב ביותר        | ממוצע               | הגרוע ביותר         | זיכרון  | יציב    | הערות  |
+| ------------------- | :----------------: | :-----------------: | :------------------: | :-----: | :-----: | :------ |
+| **מיון בועות**      | n                  | n<sup>2</sup>       | n<sup>2</sup>        | 1       | כן      |         |
+| **מיון הכנסה**      | n                  | n<sup>2</sup>       | n<sup>2</sup>        | 1       | כן      |         |
+| **מיון בחירה**      | n<sup>2</sup>      | n<sup>2</sup>       | n<sup>2</sup>        | 1       | לא      |         |
+| **מיון ערימה**      | n&nbsp;log(n)      | n&nbsp;log(n)       | n&nbsp;log(n)        | 1       | לא      |         |
+| **מיון מיזוג**      | n&nbsp;log(n)      | n&nbsp;log(n)       | n&nbsp;log(n)        | n       | כן      |         |
+| **מיון מהיר**       | n&nbsp;log(n)      | n&nbsp;log(n)       | n<sup>2</sup>        | log(n)  | לא      | מיון מהיר בדרך כלל מבוצע במקום עם O(log(n)) שטח מחסנית |
+| **מיון צדפות**      | n&nbsp;log(n)      | תלוי ברצף הפער      | n&nbsp;(log(n))<sup>2</sup>  | 1         | לא      |         |
+| **מיון ספירה**      | n + r              | n + r               | n + r                | n + r   | כן      | r - המספר הגדול ביותר במערך |
+| **מיון בסיס**       | n * k              | n * k               | n * k                | n + k   | כן      | k - אורך המפתח הארוך ביותר |
+
+## תומכי הפרויקט
+
+> אתה יכול לתמוך בפרויקט זה דרך ❤️️ [GitHub](https://github.com/sponsors/trekhleb) או ❤️️ [Patreon](https://www.patreon.com/trekhleb).
+
+[אנשים שתומכים בפרויקט זה](https://github.com/trekhleb/javascript-algorithms/blob/master/BACKERS.md) `∑ = 1`
+
+## מחבר
+
+[@trekhleb](https://trekhleb.dev)
+
+כמה [פרויקטים](https://trekhleb.dev/projects/) ו[מאמרים](https://trekhleb.dev/blog/) נוספים על JavaScript ואלגוריתמים ב-[trekhleb.dev](https://trekhleb.dev)* `B` [משחק הקפיצות](src/algorithms/uncategor * `B` [חיפוש בינארי](src/algorithms
diff --git a/README.id-ID.md b/README.id-ID.md
index b204f2a8f8..9dd908c2e6 100644
--- a/README.id-ID.md
+++ b/README.id-ID.md
@@ -23,7 +23,9 @@ _Baca ini dalam bahasa yang lain:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 _☝ Perhatikan bahwa proyek ini hanya dimaksudkan untuk tujuan pembelajaran dan riset, dan **tidak** dimaksudkan untuk digunakan sebagai produksi._
 
diff --git a/README.it-IT.md b/README.it-IT.md
index 6ac47fa5bc..d749a6aa7f 100644
--- a/README.it-IT.md
+++ b/README.it-IT.md
@@ -22,7 +22,9 @@ _Leggilo in altre lingue:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Si noti che questo progetto è destinato ad essere utilizzato solo per l'apprendimento e la ricerca e non è destinato ad essere utilizzato per il commercio.*
 
diff --git a/README.ja-JP.md b/README.ja-JP.md
index e10bb94083..acf9dee8f0 100644
--- a/README.ja-JP.md
+++ b/README.ja-JP.md
@@ -25,7 +25,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## データ構造
 
diff --git a/README.ko-KR.md b/README.ko-KR.md
index b48c43aa56..d4b0d4ef14 100644
--- a/README.ko-KR.md
+++ b/README.ko-KR.md
@@ -24,7 +24,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## 자료 구조
 
diff --git a/README.md b/README.md
index 8836940696..3e5c5cc096 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,17 @@
 # JavaScript Algorithms and Data Structures
 
 > 🇺🇦 UKRAINE [IS BEING ATTACKED](https://war.ukraine.ua/) BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED.
-> - Help Ukraine via [National Bank of Ukraine](https://bank.gov.ua/en/news/all/natsionalniy-bank-vidkriv-spetsrahunok-dlya-zboru-koshtiv-na-potrebi-armiyi)
-> - Help Ukraine via [SaveLife](https://savelife.in.ua/en/donate-en/) fund
+> - Help Ukraine via:
+>   - [Serhiy Prytula Charity Foundation](https://prytulafoundation.org/en/)
+>   - [Come Back Alive Charity Foundation](https://savelife.in.ua/en/donate-en/)
+>   - [National Bank of Ukraine](https://bank.gov.ua/en/news/all/natsionalniy-bank-vidkriv-spetsrahunok-dlya-zboru-koshtiv-na-potrebi-armiyi)
 > - More info on [war.ukraine.ua](https://war.ukraine.ua/) and [MFA of Ukraine](https://twitter.com/MFA_Ukraine)
 
 <hr/>
 
 [![CI](https://github.com/trekhleb/javascript-algorithms/workflows/CI/badge.svg)](https://github.com/trekhleb/javascript-algorithms/actions?query=workflow%3ACI+branch%3Amaster)
 [![codecov](https://codecov.io/gh/trekhleb/javascript-algorithms/branch/master/graph/badge.svg)](https://codecov.io/gh/trekhleb/javascript-algorithms)
+![repo size](https://img.shields.io/github/repo-size/trekhleb/javascript-algorithms.svg)
 
 This repository contains JavaScript based examples of many
 popular algorithms and data structures.
@@ -28,12 +31,14 @@ _Read this in other languages:_
 [_Português_](README.pt-BR.md),
 [_Русский_](README.ru-RU.md),
 [_Türkçe_](README.tr-TR.md),
-[_Italiana_](README.it-IT.md),
+[_Italiano_](README.it-IT.md),
 [_Bahasa Indonesia_](README.id-ID.md),
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md),
+[_עברית_](README.he-IL.md)
 
 *☝ Note that this project is meant to be used for learning and researching purposes
 only, and it is **not** meant to be used for production.*
@@ -45,6 +50,8 @@ be accessed and modified efficiently. More precisely, a data structure is a coll
 values, the relationships among them, and the functions or operations that can be applied to
 the data.
 
+Remember that each data has its own trade-offs. And you need to pay attention more to why you're choosing a certain data structure than to how to implement it.
+
 `B` - Beginner, `A` - Advanced
 
 * `B` [Linked List](src/data-structures/linked-list)
@@ -62,8 +69,9 @@ the data.
   * `A` [Segment Tree](src/data-structures/tree/segment-tree) - with min/max/sum range queries examples
   * `A` [Fenwick Tree](src/data-structures/tree/fenwick-tree) (Binary Indexed Tree)
 * `A` [Graph](src/data-structures/graph) (both directed and undirected)
-* `A` [Disjoint Set](src/data-structures/disjoint-set)
+* `A` [Disjoint Set](src/data-structures/disjoint-set) - a union–find data structure or merge–find set
 * `A` [Bloom Filter](src/data-structures/bloom-filter)
+* `A` [LRU Cache](src/data-structures/lru-cache/) - Least Recently Used (LRU) cache
 
 ## Algorithms
 
@@ -132,6 +140,7 @@ a set of rules that precisely define a sequence of operations.
   * `B` [Shellsort](src/algorithms/sorting/shell-sort)
   * `B` [Counting Sort](src/algorithms/sorting/counting-sort)
   * `B` [Radix Sort](src/algorithms/sorting/radix-sort)
+  * `B` [Bucket Sort](src/algorithms/sorting/bucket-sort)
 * **Linked Lists**
   * `B` [Straight Traversal](src/algorithms/linked-list/traversal)
   * `B` [Reverse Traversal](src/algorithms/linked-list/reverse-traversal)
@@ -177,6 +186,7 @@ a set of rules that precisely define a sequence of operations.
   * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem (dynamic programming and brute force versions)
   * `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach to the top (4 solutions)
   * `B` [Best Time To Buy Sell Stocks](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - divide and conquer and one-pass examples
+  * `B` [Valid Parentheses](src/algorithms/stack/valid-parentheses) - check if a string has valid parentheses (using stack)
   * `A` [N-Queens Problem](src/algorithms/uncategorized/n-queens)
   * `A` [Knight's Tour](src/algorithms/uncategorized/knight-tour)
 
@@ -189,7 +199,7 @@ algorithm is an abstraction higher than a computer program.
 * **Brute Force** - look at all the possibilities and selects the best solution
   * `B` [Linear Search](src/algorithms/search/linear-search)
   * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
-  * `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach to the top
+  * `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach the top
   * `A` [Maximum Subarray](src/algorithms/sets/maximum-subarray)
   * `A` [Travelling Salesman Problem](src/algorithms/graph/travelling-salesman) - shortest possible route that visits each city and returns to the origin city
   * `A` [Discrete Fourier Transform](src/algorithms/math/fourier-transform) - decompose a function of time (a signal) into the frequencies that make it up
@@ -220,7 +230,7 @@ algorithm is an abstraction higher than a computer program.
   * `B` [Jump Game](src/algorithms/uncategorized/jump-game)
   * `B` [Unique Paths](src/algorithms/uncategorized/unique-paths)
   * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
-  * `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach to the top
+  * `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach the top
   * `B` [Seam Carving](src/algorithms/image-processing/seam-carving) - content-aware image resizing algorithm
   * `A` [Levenshtein Distance](src/algorithms/string/levenshtein-distance) - minimum edit distance between two sequences
   * `A` [Longest Common Subsequence](src/algorithms/sets/longest-common-subsequence) (LCS)
@@ -233,9 +243,9 @@ algorithm is an abstraction higher than a computer program.
   * `A` [Bellman-Ford Algorithm](src/algorithms/graph/bellman-ford) - finding the shortest path to all graph vertices
   * `A` [Floyd-Warshall Algorithm](src/algorithms/graph/floyd-warshall) - find the shortest paths between all pairs of vertices
   * `A` [Regular Expression Matching](src/algorithms/string/regular-expression-matching)
-* **Backtracking** - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you test
-if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a
-different path of finding a solution. Normally the DFS traversal of state-space is being used.
+* **Backtracking** - similarly to brute force, try to generate all possible solutions, but each time you generate the next solution, you test
+if it satisfies all conditions and only then continue generating subsequent solutions. Otherwise, backtrack and go on a
+different path to finding a solution. Normally the DFS traversal of state-space is being used.
   * `B` [Jump Game](src/algorithms/uncategorized/jump-game)
   * `B` [Unique Paths](src/algorithms/uncategorized/unique-paths)
   * `B` [Power Set](src/algorithms/sets/power-set) - all subsets of a set
@@ -245,8 +255,8 @@ different path of finding a solution. Normally the DFS traversal of state-space
   * `A` [Combination Sum](src/algorithms/sets/combination-sum) - find all combinations that form specific sum
 * **Branch & Bound** - remember the lowest-cost solution found at each stage of the backtracking
 search, and use the cost of the lowest-cost solution found so far as a lower bound on the cost of
-a least-cost solution to the problem, in order to discard partial solutions with costs larger than the
-lowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-space
+a least-cost solution to the problem in order to discard partial solutions with costs larger than the
+lowest-cost solution found so far. Normally, BFS traversal in combination with DFS traversal of state-space
 tree is being used.
 
 ## How to use this repository
@@ -286,14 +296,14 @@ rm -rf ./node_modules
 npm i
 ```
 
-Also make sure that you're using a correct Node version (`>=14.16.0`). If you're using [nvm](https://github.com/nvm-sh/nvm) for Node version management you may run `nvm use` from the root folder of the project and the correct version will be picked up.
+Also, make sure that you're using the correct Node version (`>=16`). If you're using [nvm](https://github.com/nvm-sh/nvm) for Node version management you may run `nvm use` from the root folder of the project and the correct version will be picked up.
 
 **Playground**
 
 You may play with data-structures and algorithms in `./src/playground/playground.js` file and write
 tests for it in `./src/playground/__test__/playground.test.js`.
 
-Then just simply run the following command to test if your playground code works as expected:
+Then just, simply run the following command to test if your playground code works as expected:
 
 ```
 npm test -- 'playground'
@@ -309,7 +319,7 @@ npm test -- 'playground'
 ### Big O Notation
 
 *Big O notation* is used to classify algorithms according to how their running time or space requirements grow as the input size grows.
-On the chart below you may find most common orders of growth of algorithms specified in Big O notation.
+On the chart below, you may find the most common orders of growth of algorithms specified in Big O notation.
 
 ![Big O graphs](./assets/big-o-graph.png)
 
@@ -360,10 +370,10 @@ Below is the list of some of the most used Big O notations and their performance
 
 > You may support this project via ❤️️ [GitHub](https://github.com/sponsors/trekhleb) or ❤️️ [Patreon](https://www.patreon.com/trekhleb).
 
-[Folks who are backing this project](https://github.com/trekhleb/javascript-algorithms/blob/master/BACKERS.md) `∑ = 0`
-
-> ℹ️ A few more [projects](https://trekhleb.dev/projects/) and [articles](https://trekhleb.dev/blog/) about JavaScript and algorithms on [trekhleb.dev](https://trekhleb.dev)
+[Folks who are backing this project](https://github.com/trekhleb/javascript-algorithms/blob/master/BACKERS.md) `∑ = 1`
 
 ## Author
 
-- [@trekhleb](https://trekhleb.dev)
+[@trekhleb](https://trekhleb.dev)
+
+A few more [projects](https://trekhleb.dev/projects/) and [articles](https://trekhleb.dev/blog/) about JavaScript and algorithms on [trekhleb.dev](https://trekhleb.dev)
diff --git a/README.pl-PL.md b/README.pl-PL.md
index 52ba75a5e4..56617030ab 100644
--- a/README.pl-PL.md
+++ b/README.pl-PL.md
@@ -26,7 +26,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## Struktury Danych
 
diff --git a/README.pt-BR.md b/README.pt-BR.md
index a937e8c0bf..5b16798bc9 100644
--- a/README.pt-BR.md
+++ b/README.pt-BR.md
@@ -26,7 +26,9 @@ _Leia isto em outros idiomas:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## Estrutura de Dados
 
diff --git a/README.ru-RU.md b/README.ru-RU.md
index 541e1d2703..939ed46700 100644
--- a/README.ru-RU.md
+++ b/README.ru-RU.md
@@ -23,7 +23,9 @@ _Читать на других языках:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Замечание: этот репозиторий предназначен для учебно-исследовательских целей (**не** для использования в продакшн-системах).*
 
diff --git a/README.tr-TR.md b/README.tr-TR.md
index 805bf7b431..53600480db 100644
--- a/README.tr-TR.md
+++ b/README.tr-TR.md
@@ -23,7 +23,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Not, bu proje araştırma ve öğrenme amacı ile yapılmış
 olup üretim için **yapılmamıştır**.*
diff --git a/README.uk-UA.md b/README.uk-UA.md
index dd129b2c3c..5ee5f7cbcc 100644
--- a/README.uk-UA.md
+++ b/README.uk-UA.md
@@ -23,7 +23,9 @@ _Вивчення матеріалу на інших мовах:_
 [_Bahasa Indonesia_](README.id-ID.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Зверніть увагу! Даний проект призначений лише для навчальних та дослідницьких цілей, і він **не** призначений для виробництва (продакшн).*
 
diff --git a/README.uz-UZ.md b/README.uz-UZ.md
new file mode 100644
index 0000000000..114a4de9cc
--- /dev/null
+++ b/README.uz-UZ.md
@@ -0,0 +1,359 @@
+# JavaScript algoritmlari va ma'lumotlar tuzilmalari
+
+[![CI](https://github.com/trekhleb/javascript-algorithms/workflows/CI/badge.svg)](https://github.com/trekhleb/javascript-algorithms/actions?query=workflow%3ACI+branch%3Amaster)
+[![codecov](https://codecov.io/gh/trekhleb/javascript-algorithms/branch/master/graph/badge.svg)](https://codecov.io/gh/trekhleb/javascript-algorithms)
+![repo size](https://img.shields.io/github/repo-size/trekhleb/javascript-algorithms.svg)
+
+Bu repozitoriyada JavaScript-ga asoslangan ko'plab mashhur algoritmlar
+va ma'lumotlar tuzilmalarining namunalari mavjud.
+
+Har bir algoritm va ma'lumotlar tuzilmasining alohida README fayli
+bo'lib, unda tegishli tushuntirishlar va qo'shimcha o'qish uchun
+havolalar (shu jumladan YouTube videolariga ham havolalar) mavjud.
+
+_Read this in other languages:_
+[_简体中文_](README.zh-CN.md),
+[_繁體中文_](README.zh-TW.md),
+[_한국어_](README.ko-KR.md),
+[_日本語_](README.ja-JP.md),
+[_Polski_](README.pl-PL.md),
+[_Français_](README.fr-FR.md),
+[_Español_](README.es-ES.md),
+[_Português_](README.pt-BR.md),
+[_Русский_](README.ru-RU.md),
+[_Türkçe_](README.tr-TR.md),
+[_Italiana_](README.it-IT.md),
+[_Bahasa Indonesia_](README.id-ID.md),
+[_Українська_](README.uk-UA.md),
+[_Arabic_](README.ar-AR.md),
+[_Tiếng Việt_](README.vi-VN.md),
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
+
+Yodda tuting, bu loyiha faqat o'quv va tadqiqot maqsadida ishlatilishi
+uchun mo'ljallangan va ishlab chiqarishda ishlatilishi **mumkin emas**.
+
+## Ma'lumotlar tuzilmalari
+
+Ma'lumotlar tuzilmasi - bu kompyuterda ma'lumotlarni samarali tarzda
+olish va o'zgartirish uchun ularni tashkil etish va saqlashning ma'lum
+bir usuli. Ayniqsa, ma'lumotlar tuzilmasi ma'lumot qiymatlarining
+to'plami, ular orasidagi munosabatlar va ma'lumotlarga qo'llanilishi
+mumkin bo'lgan funksiyalar yoki operatsiyalardir.
+
+`B` - Boshlang'ich, `A` - Ilg'or
+
+- `B` [Bog'langan ro'yxat](src/data-structures/linked-list)
+- `B` [Ikki marta bog'langan ro'yxat](src/data-structures/doubly-linked-list)
+- `B` [Navbat](src/data-structures/queue)
+- `B` [Stek](src/data-structures/stack)
+- `B` [Hash jadvali](src/data-structures/hash-table)
+- `B` [Heap](src/data-structures/heap) - maksimal va minimal heap versiyalari
+- `B` [Ustuvor navbat](src/data-structures/priority-queue)
+- `A` [Trie](src/data-structures/trie)
+- `A` [Daraxt](src/data-structures/tree)
+  - `A` [Ikkilik qidiruv daraxt](src/data-structures/tree/binary-search-tree)
+  - `A` [AVL daraxt](src/data-structures/tree/avl-tree)
+  - `A` [Qizil-qora daraxt](src/data-structures/tree/red-black-tree)
+  - `A` [Segment daraxt](src/data-structures/tree/segment-tree) - min/max/sum diapazon so'rovlari bilan misollar
+  - `A` [Fenwick daraxt](src/data-structures/tree/fenwick-tree) (ikkilik indeksli daraxt)
+- `A` [Graf](src/data-structures/graph) (yo'naltirilgan hamda yo'naltirilmagan)
+- `A` [Ajratilgan to'plam](src/data-structures/disjoint-set) - union-find ma'lumotlar strukturasi yoki merge-find to'plami
+- `A` [Bloom filtri](src/data-structures/bloom-filter)
+- `A` [LRU keshi](src/data-structures/lru-cache/) - Eng kam ishlatilgan (LRU) keshi
+
+## Algoritmlar
+
+Algoritm muammolar sinfini qanday hal qilishning aniq spetsifikatsiyasi. Bu operatsiyalar ketma-ketligini aniqlaydigan qoidalar to'plami.
+
+`B` - Boshlang'ich, `A` - Ilg'or
+
+### Mavzu bo'yicha algoritmlar
+
+- **Matematika**
+  - `B` [Bit manipulatsiyasi](src/algorithms/math/bits) - bitlarni qo'yish/olish/yangilash/tozalash, ikkilikka ko'paytirish/bo'lish, manfiy qilish va hokazo.
+  - `B` [Ikkilik suzuvchi nuqta](src/algorithms/math/binary-floating-point) - suzuvchi nuqtali sonlarning ikkilik tasviri.
+  - `B` [Faktorial](src/algorithms/math/factorial)
+  - `B` [Fibonachchi raqam](src/algorithms/math/fibonacci) - klassik va yopiq shakldagi versiyalar
+  - `B` [Asosiy omillar](src/algorithms/math/prime-factors) - tub omillarni topish va ularni Xardi-Ramanujan teoremasi yordamida sanash
+  - `B` [Birlamchilik testi](src/algorithms/math/primality-test) (sinov bo'linish usuli)
+  - `B` [Evklid algoritmi](src/algorithms/math/euclidean-algorithm) - eng katta umumiy bo'luvchini (EKUB) hisoblash
+  - `B` [Eng kichik umumiy karrali](src/algorithms/math/least-common-multiple) (EKUK)
+  - `B` [Eratosfen elagi](src/algorithms/math/sieve-of-eratosthenes) - berilgan chegaragacha barcha tub sonlarni topish
+  - `B` [Ikkining darajasimi](src/algorithms/math/is-power-of-two) - raqamning ikkining darajasi ekanligini tekshirish (sodda va bitli algoritmlar)
+  - `B` [Paskal uchburchagi](src/algorithms/math/pascal-triangle)
+  - `B` [Kompleks sonlar](src/algorithms/math/complex-number) - kompleks sonlar va ular bilan asosiy amallar
+  - `B` [Radian & Daraja](src/algorithms/math/radian) - radianlarni darajaga va orqaga aylantirish
+  - `B` [Tez ko'tarish](src/algorithms/math/fast-powering)
+  - `B` [Horner metodi](src/algorithms/math/horner-method) - polinomlarni baholash
+  - `B` [Matritsalar](src/algorithms/math/matrix) - matritsalar va asosiy matritsa operatsiyalari (ko'paytirish, transpozitsiya va boshqalar).
+  - `B` [Evklid masofasi](src/algorithms/math/euclidean-distance) - ikki nuqta/vektor/matritsa orasidagi masofa
+  - `A` [Butun sonlarni bo'lish](src/algorithms/math/integer-partition)
+  - `A` [Kvadrat ildiz](src/algorithms/math/square-root) - Nyuton metodi
+  - `A` [Liu Hui π algoritmi](src/algorithms/math/liu-hui) - N-gonlarga asoslangan π ning taxminiy hisoblari
+  - `A` [Diskret Furye transformatsiyasi](src/algorithms/math/fourier-transform) - vaqt funksiyasini (signalni) uni tashkil etuvchi chastotalarga ajratish
+- **Sets**
+  - `B` [Karteziya maxsuloti](src/algorithms/sets/cartesian-product) - bir nechta to'plamlarning ko'paytmasi
+  - `B` [Fisher–Yates Shuffle](src/algorithms/sets/fisher-yates) - chekli ketma-ketlikni tasodifiy almashtirish
+  - `A` [Power Set](src/algorithms/sets/power-set) - to'plamning barcha kichik to'plamlari (bitwise, backtracking va kaskadli echimlar)
+  - `A` [Permutatsiyalar](src/algorithms/sets/permutations) (takroriyalash bilan va takroriyalashsiz)
+  - `A` [Kombinatsiyalar](src/algorithms/sets/combinations) (takroriyalash bilan va takroriyalashsiz)
+  - `A` [Eng uzun umumiy ketma-ketlik](src/algorithms/sets/longest-common-subsequence) (LCS)
+  - `A` [Eng uzun ortib boruvchi ketma-ketlik](src/algorithms/sets/longest-increasing-subsequence)
+  - `A` [Eng qisqa umumiy ketma-ketlik](src/algorithms/sets/shortest-common-supersequence) (SCS)
+  - `A` [Knapsack muammosi](src/algorithms/sets/knapsack-problem) - "0/1" va "Bir-biriga bog'lanmagan"
+  - `A` [Maksimal kichik massiv](src/algorithms/sets/maximum-subarray) - Toʻliq kuch va dinamik dasturlash (Kadane usuli) versiyalari
+  - `A` [Kombinatsiya yig'indisi](src/algorithms/sets/combination-sum) - ma'lum summani tashkil etuvchi barcha kombinatsiyalarni topish
+- **Stringlar**
+  - `B` [Hamming masofasi](src/algorithms/string/hamming-distance) - belgilarning bir-biridan farq qiladigan pozitsiyalar soni
+  - `B` [Palindrom](src/algorithms/string/palindrome) - satrning teskari tomoni ham bir xil ekanligini tekshirish
+  - `A` [Levenshtein masofasi](src/algorithms/string/levenshtein-distance) - ikki ketma-ketlik o'rtasidagi minimal tahrirlash masofasi
+  - `A` [Knuth–Morris–Pratt Algoritmi](src/algorithms/string/knuth-morris-pratt) (KMP Algoritmi) - kichik qatorlarni qidirish (mosh keluvchi naqshni qidirish)
+  - `A` [Z Algoritmi](src/algorithms/string/z-algorithm) - kichik qatorlarni qidirish (mosh keluvchi naqshni qidirish)
+  - `A` [Rabin Karp Algoritmi](src/algorithms/string/rabin-karp) - kichik qatorlarni qidirish
+  - `A` [Eng uzun umumiy kichik matn](src/algorithms/string/longest-common-substring)
+  - `A` [Regulyar ifoda moslashuvi](src/algorithms/string/regular-expression-matching) (RegEx)
+- **Qidiruvlar**
+  - `B` [Linear qidirish](src/algorithms/search/linear-search)
+  - `B` [Jump qidirish](src/algorithms/search/jump-search) (yoki Blok qidirish) - saralangan qatorda qidirish
+  - `B` [Ikkilik qidirish](src/algorithms/search/binary-search) - saralangan qatorda qidirish
+  - `B` [Interpolatsiya qidirish](src/algorithms/search/interpolation-search) - bir tekis taqsimlangan saralangan qatorda qidirish
+- **Tartiblash**
+  - `B` [Pufakcha tartiblash](src/algorithms/sorting/bubble-sort)
+  - `B` [Tanlash tartibi](src/algorithms/sorting/selection-sort)
+  - `B` [Kiritish tartibi](src/algorithms/sorting/insertion-sort)
+  - `B` [Heap tartibi](src/algorithms/sorting/heap-sort)
+  - `B` [Birlashtirish tartibi](src/algorithms/sorting/merge-sort)
+  - `B` [Tezkor saralash](src/algorithms/sorting/quick-sort) - joyida va joyida bo'lmagan amalga oshirish
+  - `B` [Shell tartiblash](src/algorithms/sorting/shell-sort)
+  - `B` [Sanash tartibi](src/algorithms/sorting/counting-sort)
+  - `B` [Radiksli tartiblash](src/algorithms/sorting/radix-sort)
+  - `B` [Bucket tartiblash](src/algorithms/sorting/bucket-sort)
+- **Bog'langan ro'yhatlar**
+  - `B` [To'g'ri traversal](src/algorithms/linked-list/traversal)
+  - `B` [Teskari traversal](src/algorithms/linked-list/reverse-traversal)
+- **Daraxtlar**
+  - `B` [Birinchi-pastga qarab qidirish](src/algorithms/tree/depth-first-search) (Depth-First Search)
+  - `B` [Birinchi-yonga qarab qidirish](src/algorithms/tree/breadth-first-search) (Breadth-First Search)
+- **Grafiklar**
+  - `B` [Birinchi-pastga qarab qidirish](src/algorithms/graph/depth-first-search) (Depth-First Search)
+  - `B` [Birinchi-yonga qarab qidirish](src/algorithms/graph/breadth-first-search) (Breadth-First Search)
+  - `B` [Kruskal Algoritmi](src/algorithms/graph/kruskal) - og'irlikdagi yo'naltirilmagan grafik uchun Minimal kengayuvchi daraxtni (MST) topish
+  - `A` [Dijkstra Algoritmi](src/algorithms/graph/dijkstra) - grafikning bir cho'qqisidan qolgan barcha nuqtalarga eng qisqa yo'llarni topish
+  - `A` [Bellman-Ford Algoritmi](src/algorithms/graph/bellman-ford) - grafikning bir cho'qqisidan qolgan barcha nuqtalarga eng qisqa yo'llarni topish
+  - `A` [Floyd-Warshall Algoritmi](src/algorithms/graph/floyd-warshall) - grafikning barcha uchlari orasidagi eng qisqa masofalarni topish
+  - `A` [Siklni aniqlash](src/algorithms/graph/detect-cycle) - yo'naltirilgan va yo'naltirilmagan grafiklar uchun (DFS va Disjoint Set-ga asoslangan versiyalar)
+  - `A` [Prim Algoritmi](src/algorithms/graph/prim) - og'irlikdagi yo'naltirilmagan grafik uchun Minimal kengayuvchi daraxtni (MST) topish
+  - `A` [Topologik saralash](src/algorithms/graph/topological-sorting) - DFS metodi
+  - `A` [Artikulyatsiya nuqtalari](src/algorithms/graph/articulation-points) - Tarjan algoritmi (DFS asosida)
+  - `A` [Ko'priklar](src/algorithms/graph/bridges) - DFS asosidagi algoritm
+  - `A` [Eyler yo'li va Eyler sxemasi](src/algorithms/graph/eulerian-path) - Fleury algoritmi - Har bir chekkaga bir marta tashrif buyurish
+  - `A` [Gamilton sikli](src/algorithms/graph/hamiltonian-cycle) - Har bir cho'qqiga bir marta tashrif buyurish
+  - `A` [Kuchli bog'langan komponentlar](src/algorithms/graph/strongly-connected-components) - Kosaraju algoritmi
+  - `A` [Sayohatchi sotuvchi muammosi](src/algorithms/graph/travelling-salesman) - har bir shaharga tashrif buyuradigan va kelib chiqqan shaharga qaytib keladigan eng qisqa yo'l
+- **Kriptografiya**
+  - `B` [Polynomial Hash](src/algorithms/cryptography/polynomial-hash) - polinomga asoslangan hash funktsiyasi
+  - `B` [Rail Fence Cipher](src/algorithms/cryptography/rail-fence-cipher) - xabarlarni kodlash uchun transpozitsiya shifrlash algoritmi
+  - `B` [Caesar Cipher](src/algorithms/cryptography/caesar-cipher) - oddiy almashtirish shifridir
+  - `B` [Hill Cipher](src/algorithms/cryptography/hill-cipher) - chiziqli algebraga asoslangan almashtirish shifri
+- **Machine Learning**
+  - `B` [NanoNeuron](https://github.com/trekhleb/nano-neuron) - Mashinalar aslida qanday o'rganishi mumkinligini ko'rsatadigan 7 ta oddiy JS funksiyasi (forward/backward tarqalish)
+  - `B` [k-NN](src/algorithms/ml/knn) - eng yaqin qo'shnilarni tasniflash algoritmi
+  - `B` [k-Means](src/algorithms/ml/k-means) - k-Means kalsterlash algoritmi
+- **Tasvirga ishlov berish**
+  - `B` [Seam Carving](src/algorithms/image-processing/seam-carving) - kontentga moslashuvchan rasm o'lchamini o'zgartirish algoritmi
+- **Statistikalar**
+  - `B` [Weighted Random](src/algorithms/statistics/weighted-random) - elementlarning og'irligi asosida ro'yxatdan tasodifiy elementni tanlash
+- **Evolyutsion algoritmlar**
+  - `A` [Genetik algoritm](https://github.com/trekhleb/self-parking-car-evolution) - avtoturargohni o'rgatish uchun genetik algoritm qanday qo'llanilishiga misol.
+- **Kategoriyasiz**
+  - `B` [Xanoy minorasi](src/algorithms/uncategorized/hanoi-tower)
+  - `B` [Kvadrat matritsaning aylanishi](src/algorithms/uncategorized/square-matrix-rotation) - joyidagi algoritm
+  - `B` [Sakrash o'yini](src/algorithms/uncategorized/jump-game) - orqaga qaytish, dinamik dasturlash (yuqoridan pastga + pastdan yuqoriga) va ochko'z misollar
+  - `B` [Noyob yo'llar](src/algorithms/uncategorized/unique-paths) - orqaga qaytish, dinamik dasturlash va Paskal uchburchagiga asoslangan misolla
+  - `B` [Yomg'ir teraslari](src/algorithms/uncategorized/rain-terraces) - yomg'ir suvini ushlab turish muammosi (dinamik dasturlash va qo'pol kuch versiyalari)
+  - `B` [Rekursiv zinapoya](src/algorithms/uncategorized/recursive-staircase) - yuqoriga chiqish yo'llari sonini hisoblash (4 ta echim)
+  - `B` [Aksiyalarni sotib olish va sotish uchun eng yaxshi vaqt](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - bo'linib-zabt etish va bir marta o'tish misollari
+  - `A` [N-Queens Muommosi](src/algorithms/uncategorized/n-queens)
+  - `A` [Ritsar sayohati](src/algorithms/uncategorized/knight-tour)
+
+### Paradigma bo'yicha algoritmlar
+
+Algorithmic paradigm - bu algoritmlar sinfini loyihalashtirishga asos bo'lib xizmat qiladigan umumiy usul yoki yondashuv. Bu algoritm tushunchasidan yuqori darajadagi abstraktsiya bo'lib, algoritm kompyuter dasturi tushunchasidan yuqori darajadagi abstraktsiya bo'lgani kabi.
+
+- **Brute Force** - barcha imkoniyatlarni ko'rib chiqib va eng yaxshi echimni tanlash
+  - `B` [Chiziqli qidirish](src/algorithms/search/linear-search)
+  - `B` [Yomg'irli teraslar](src/algorithms/uncategorized/rain-terraces) - yomg'ir suvini to'plash muammosi
+  - `B` [Rekursiv zinapoya](src/algorithms/uncategorized/recursive-staircase) - cho'qqiga chiqish yo'llari sonini hisoblash
+  - `A` [Maksimal kichik massiv](src/algorithms/sets/maximum-subarray)
+  - `A` [Sayohatchi sotuvchi muammosi](src/algorithms/graph/travelling-salesman) - har bir shaharga tashrif buyuradigan va kelib chiqqan shaharga qaytib keladigan eng qisqa yo'l
+  - `A` [Diskret Furye transformatsiyasi](src/algorithms/math/fourier-transform) - vaqt funksiyasini (signalni) uni tashkil etuvchi chastotalarga ajratish
+- **Greedy** - kelajakni o'ylamasdan, hozirgi vaqtda eng yaxshi variantni tanlash
+  - `B` [Sakrash o'yini](src/algorithms/uncategorized/jump-game)
+  - `A` [Bog'lanmagan yukxalta muammosi](src/algorithms/sets/knapsack-problem)
+  - `A` [Dijkstra Algoritmi](src/algorithms/graph/dijkstra) - grafikning bir cho'qqisidan qolgan barcha nuqtalarga eng qisqa yo'llarni topish
+  - `A` [Prim Algoritmi](src/algorithms/graph/prim) - og'irlikdagi yo'naltirilmagan grafik uchun Minimal kengayuvchi daraxtni (MST) topish
+  - `A` [Kruskal Algoritmi](src/algorithms/graph/kruskal) - og'irlikdagi yo'naltirilmagan grafik uchun Minimal kengayuvchi daraxtni (MST) topish
+- **Divide and Conquer** - muammoni kichikroq qismlarga bo'lib va keyin bu qismlarni hal qilish
+
+  - `B` [Ikkilik qidiruv](src/algorithms/search/binary-search)
+  - `B` [Xanoy minorasi](src/algorithms/uncategorized/hanoi-tower)
+  - `B` [Paskal uchburchagi](src/algorithms/math/pascal-triangle)
+  - `B` [Evklid Algoritmi](src/algorithms/math/euclidean-algorithm) - eng katta umumiy bo'luvchini (EKUB) hisoblash
+  - `B` [Birlashtirish tartibi](src/algorithms/sorting/merge-sort)
+  - `B` [Tezkor saralash](src/algorithms/sorting/quick-sort)
+  - `B` [Birinchi-pastga qarab qidirish daraxti](src/algorithms/tree/depth-first-search) (DFS)
+  - `B` [Birinchi-pastga qarab qidirish grafigi](src/algorithms/graph/depth-first-search) (DFS)
+  - `B` [Matritsalar](src/algorithms/math/matrix) - turli shakldagi matritsalarni hosil qilish va kesib o'tish
+  - `B` [Sakrash o'yini](src/algorithms/uncategorized/jump-game)
+  - `B` [Tez ko'tarish](src/algorithms/math/fast-powering)
+  - `B` [Aksiyalarni sotib olish va sotish uchun eng yaxshi vaqt](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - bo'linib-zabt etish va bir marta o'tish misollari
+  - `A` [Permutatsiyalar](src/algorithms/sets/permutations) (takroriyalash bilan va takroriyalashsiz)
+  - `A` [Kombinatsiyalar](src/algorithms/sets/combinations) (takroriyalash bilan va takroriyalashsiz)
+  - `A` [Maksimal kichik massiv](src/algorithms/sets/maximum-subarray)
+
+- **Dinamik dasturlash** - ilgari topilgan kichik yechimlar yordamida yechim yaratish
+  - `B` [Fibonachchi raqam](src/algorithms/math/fibonacci)
+  - `B` [Sakrash o'yini](src/algorithms/uncategorized/jump-game)
+  - `B` [Noyob yo'llar](src/algorithms/uncategorized/unique-paths)
+  - `B` [Yomg'ir teraslari](src/algorithms/uncategorized/rain-terraces) - yomg'ir suvini to'plash muammosi
+  - `B` [Recursive Staircase](src/algorithms/uncategorized/recursive-staircase) - count the number of ways to reach to the top
+  - `B` [Seam Carving](src/algorithms/image-processing/seam-carving) - kontentga moslashuvchan rasm o'lchamini o'zgartirish algoritmi
+  - `A` [Levenshtein masofasi](src/algorithms/string/levenshtein-distance) - ikki ketma-ketlik o'rtasidagi minimal tahrirlash masofasi
+  - `A` [Eng uzun umumiy ketma-ketlik](src/algorithms/sets/longest-common-subsequence) (LCS)
+  - `A` [Eng uzun umumiy kichik matn](src/algorithms/string/longest-common-substring)
+  - `A` [Eng uzun ortib boruvchi ketma-ketlik](src/algorithms/sets/longest-increasing-subsequence)
+  - `A` [Eng qisqa umumiy ketma-ketlik](src/algorithms/sets/shortest-common-supersequence)
+  - `A` [0/1 Knapsak muommosi](src/algorithms/sets/knapsack-problem)
+  - `A` [Butun sonlarni bo'lish](src/algorithms/math/integer-partition)
+  - `A` [Maksimal kichik massiv](src/algorithms/sets/maximum-subarray)
+  - `A` [Bellman-Ford Algoritmi](src/algorithms/graph/bellman-ford) - grafikning bir cho'qqisidan qolgan barcha nuqtalarga eng qisqa yo'llarni topish
+  - `A` [Floyd-Warshall Algoritmi](src/algorithms/graph/floyd-warshall) -grafikning barcha uchlari orasidagi eng qisqa masofalarni topish
+  - `A` [Regulyar ifoda moslashuvi](src/algorithms/string/regular-expression-matching)
+- **Backtracking** - brute forcega o'xshab, barcha mumkin bo'lgan yechimlarni generatsiya qilishga harakat qiladi, lekin har safar keyingi yechimni yaratganingizda, yechim barcha shartlarga javob beradimi yoki yo'qligini tekshirasiz va shundan keyingina keyingi yechimlarni ishlab chiqarishni davom ettirasiz. Aks holda, orqaga qaytib, yechim topishning boshqa yo'liga o'tasiz. Odatda state-space ning DFS-qidiruvi ishlatiladi.
+  - `B` [Sakrash o'yini](src/algorithms/uncategorized/jump-game)
+  - `B` [Noyob yo'llar](src/algorithms/uncategorized/unique-paths)
+  - `B` [Power Set](src/algorithms/sets/power-set) - to'plamning barcha kichik to'plamlari
+  - `A` [Gamilton sikli](src/algorithms/graph/hamiltonian-cycle) - Har bir cho'qqiga bir marta tashrif buyurish
+  - `A` [N-Queens muommosi](src/algorithms/uncategorized/n-queens)
+  - `A` [Ritsar sayohati](src/algorithms/uncategorized/knight-tour)
+  - `A` [Kombinatsiya yig'indisi](src/algorithms/sets/combination-sum) - ma'lum summani tashkil etuvchi barcha kombinatsiyalarni topish
+- **Branch & Bound** - shu paytgacha topilgan eng arzon echimdan kattaroq xarajatlarga ega qisman echimlarni bekor qilish uchun, backtracking qidiruvining har bir bosqichida topilgan eng arzon echimni eslab qoling va shu paytgacha topilgan eng arzon yechim narxidan muammoni eng kam xarajatli yechim narxining past chegarasi sifatida foydalaning. Odatda state-space daraxtining DFS o'tishi bilan birgalikda BFS traversal qo'llaniladi.
+
+## Ushbu repozitoriyadan qanday foydalanish kerak
+
+**Barcha dependensiylarni o'rnating**
+
+```
+npm install
+```
+
+**ESLint ni ishga tushiring**
+
+Kod sifatini tekshirish uchun ESLint ni ishga tushirishingiz mumkin.
+
+```
+npm run lint
+```
+
+**Barcha testlarni ishga tushuring**
+
+```
+npm test
+```
+
+**Testlarni nom bo'yicha ishga tushirish**
+
+```
+npm test -- 'LinkedList'
+```
+
+**Muammolarni bartaraf qilish (Troubleshooting)**
+
+Agar linting yoki sinov muvaffaqiyatsiz bo'lsa, `node_modules` papkasini o'chirib, npm paketlarini qayta o'rnatishga harakat qiling:
+
+```
+rm -rf ./node_modules
+npm i
+```
+
+Shuningdek, to'g'ri Node versiyasidan foydalanayotganingizga ishonch hosil qiling (`>=16`). Agar Node versiyasini boshqarish uchun [nvm](https://github.com/nvm-sh/nvm) dan foydalanayotgan bo'lsangiz, loyihaning ildiz papkasidan `nvm use` ni ishga tushiring va to'g'ri versiya tanlanadi.
+
+**O'yin maydoni (Playground)**
+
+`./src/playground/playground.js` faylida ma'lumotlar strukturalari va algoritmlar bilan o'ynashingiz, `./src/playground/test/playground.test.js` faylida esa ular uchun testlar yozishingiz mumkin.
+
+Shundan so'ng, playground kodingiz kutilgandek ishlashini tekshirish uchun quyidagi buyruqni ishga tushirishingiz kifoya:
+
+```
+npm test -- 'playground'
+```
+
+## Foydali ma'lumotlar
+
+### Manbalar
+
+- [▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
+- [✍🏻 Data Structure Sketches](https://okso.app/showcase/data-structures)
+
+### Big O Notation
+
+_Big O notation_ algoritmlarni kirish hajmi oshgani sayin ularning ishlash vaqti yoki bo'sh joy talablari qanday o'sishiga qarab tasniflash uchun ishlatiladi. Quyidagi jadvalda siz Big O notatsiyasida ko'rsatilgan algoritmlarning o'sishining eng keng tarqalgan tartiblarini topishingiz mumkin.
+
+![Big O grafiklar](./assets/big-o-graph.png)
+
+Manba: [Big O Cheat Sheet](http://bigocheatsheet.com/).
+
+Quyida eng ko'p qo'llaniladigan Big O notatsiyalarining ro'yxati va ularning kirish ma'lumotlarining turli o'lchamlariga nisbatan ishlashini taqqoslash keltirilgan.
+
+| Big O Notatsiya | Turi         | 10 ta element uchun hisob-kitoblar | 100 ta element uchun hisob-kitoblar | 1000 ta element uchun hisob-kitoblar |
+| --------------- | ------------ | ---------------------------------- | ----------------------------------- | ------------------------------------ |
+| **O(1)**        | O'zgarmas    | 1                                  | 1                                   | 1                                    |
+| **O(log N)**    | Logarifmik   | 3                                  | 6                                   | 9                                    |
+| **O(N)**        | Chiziqli     | 10                                 | 100                                 | 1000                                 |
+| **O(N log N)**  | n log(n)     | 30                                 | 600                                 | 9000                                 |
+| **O(N^2)**      | Kvadrat      | 100                                | 10000                               | 1000000                              |
+| **O(2^N)**      | Eksponensial | 1024                               | 1.26e+29                            | 1.07e+301                            |
+| **O(N!)**       | Faktorial    | 3628800                            | 9.3e+157                            | 4.02e+2567                           |
+
+### Ma'lumotlar tuzilmalarining operatsiyalari murakkabligi
+
+| Ma'lumotlar tuzilmalari     | Kirish | Qidirish | Kiritish | O'chirish | Izohlar                                                    |
+| --------------------------- | :----: | :------: | :------: | :-------: | :--------------------------------------------------------- |
+| **Massiv**                  |   1    |    n     |    n     |     n     |                                                            |
+| **Stak**                    |   n    |    n     |    1     |     1     |                                                            |
+| **Navbat**                  |   n    |    n     |    1     |     1     |                                                            |
+| **Bog'langan ro'yhat**      |   n    |    n     |    1     |     n     |                                                            |
+| **Hash jadval**             |   -    |    n     |    n     |     n     | Mukammal xash funksiyasi bo'lsa, xarajatlar O (1) bo'ladi. |
+| **Ikkilik qidiruv daraxti** |   n    |    n     |    n     |     n     | Balanslangan daraxt narxida O(log(n)) bo'ladi.             |
+| **B-daraxti**               | log(n) |  log(n)  |  log(n)  |  log(n)   |                                                            |
+| **Qizil-qora daraxt**       | log(n) |  log(n)  |  log(n)  |  log(n)   |                                                            |
+| **AVL Daraxt**              | log(n) |  log(n)  |  log(n)  |  log(n)   |                                                            |
+| **Bloom filtri**            |   -    |    1     |    1     |     -     | Qidiruv paytida noto'g'ri pozitivlar bo'lishi mumkin       |
+
+### Massivlarni saralash algoritmlarining murakkabligi
+
+| Nomi                      |  Eng yaxshi   |          O'rta          |          Eng yomon          | Xotira | Barqaror | Izohlar                                                                      |
+| ------------------------- | :-----------: | :---------------------: | :-------------------------: | :----: | :------: | :--------------------------------------------------------------------------- |
+| **Pufakcha tartiblash**   |       n       |      n<sup>2</sup>      |        n<sup>2</sup>        |   1    |    Ha    |                                                                              |
+| **Kiritish tartibi**      |       n       |      n<sup>2</sup>      |        n<sup>2</sup>        |   1    |    Ha    |                                                                              |
+| **Tanlash tartibi**       | n<sup>2</sup> |      n<sup>2</sup>      |        n<sup>2</sup>        |   1    |   Yo'q   |                                                                              |
+| **Heap tartibi**          | n&nbsp;log(n) |      n&nbsp;log(n)      |        n&nbsp;log(n)        |   1    |   Yo'q   |                                                                              |
+| **Birlashtirish tartibi** | n&nbsp;log(n) |      n&nbsp;log(n)      |        n&nbsp;log(n)        |   n    |    Ha    |                                                                              |
+| **Tezkor saralash**       | n&nbsp;log(n) |      n&nbsp;log(n)      |        n<sup>2</sup>        | log(n) |   Yo'q   | Tezkor saralash odatda O(log(n)) stek maydoni bilan joyida amalga oshiriladi |
+| **Shell tartiblash**      | n&nbsp;log(n) | depends on gap sequence | n&nbsp;(log(n))<sup>2</sup> |   1    |   Yo'q   |                                                                              |
+| **Sanash tartibi**        |     n + r     |          n + r          |            n + r            | n + r  |    Ha    | r - massivdagi eng katta raqam                                               |
+| **Radiksli tartiblash**   |    n \* k     |         n \* k          |           n \* k            | n + k  |    Ha    | k - eng uzun kalitning uzunligi                                              |
+
+## Loyihani qo'llab-quvvatlovchilar
+
+> Siz ushbu loyihani ❤️️ [GitHub](https://github.com/sponsors/trekhleb) yoki ❤️️ [Patreon](https://www.patreon.com/trekhleb) orqali qo'llab-quvvatlashingiz mumkin.
+
+[Ushbu loyihani qo'llab-quvvatlagan odamlar](https://github.com/trekhleb/javascript-algorithms/blob/master/BACKERS.md) `∑ = 1`
+
+## Muallif
+
+[@trekhleb](https://trekhleb.dev)
+
+A few more [projects](https://trekhleb.dev/projects/) and [articles](https://trekhleb.dev/blog/) about JavaScript and algorithms on [trekhleb.dev](https://trekhleb.dev)
diff --git a/README.vi-VN.md b/README.vi-VN.md
index a5cbb72e07..4c35f467f8 100644
--- a/README.vi-VN.md
+++ b/README.vi-VN.md
@@ -25,6 +25,7 @@ _Đọc bằng ngôn ngữ khác:_
 [_Bahasa Indonesia_](README.id-ID.md),
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md)
+[_עברית_](README.he-IL.md)
 
 *☝ Dự án này chỉ được sử dụng cho mục đích học tập và nghiên cứu, **không** được dùng
 cho mục đích thương mại.*
@@ -97,19 +98,19 @@ quy tắc xác định chính xác một chuỗi phép toán.
   * `A` [Dãy con chung ngắn nhất](src/algorithms/sets/shortest-common-supersequence) (SCS)
   * `A` [Bài toán xếp ba lô](src/algorithms/sets/knapsack-problem) - dạng 0-1 và không bị chặn
   * `A` [Mảng con lớn nhất](src/algorithms/sets/maximum-subarray) - phiên bản vét cạn và quy hoạch động (Kadane)
-  * `A` [Tổ hợp của tổng](src/algorithms/sets/combination-sum) - tìm tất cả các tổ hợp tạo thành tổng cụ thể 
+  * `A` [Tổ hợp của tổng](src/algorithms/sets/combination-sum) - tìm tất cả các tổ hợp tạo thành tổng cụ thể
 * **Chuỗi**
-  * `B` [Khoảng cách Hamming](src/algorithms/string/hamming-distance) - số các vị trí các ký hiệu khác nhau 
+  * `B` [Khoảng cách Hamming](src/algorithms/string/hamming-distance) - số các vị trí các ký hiệu khác nhau
   * `A` [Khoảng cách Levenshtein](src/algorithms/string/levenshtein-distance) - khoảng cách thay đổi nhỏ nhất giữa hai chuỗi ký tự
-  * `A` [Thuật toán Knuth–Morris–Pratt](src/algorithms/string/knuth-morris-pratt) (thuật toán KMP) - tìm chuỗi con (đối sánh mẫu) 
+  * `A` [Thuật toán Knuth–Morris–Pratt](src/algorithms/string/knuth-morris-pratt) (thuật toán KMP) - tìm chuỗi con (đối sánh mẫu)
   * `A` [Thuật toán Z](src/algorithms/string/z-algorithm) - tìm chuỗi con (đối sánh mẫu)
   * `A` [Thuật toán Rabin Karp](src/algorithms/string/rabin-karp) - tìm chuỗi con
   * `A` [Xâu con chung dài nhất](src/algorithms/string/longest-common-substring)
   * `A` [Phối biểu thức chính quy](src/algorithms/string/regular-expression-matching)
 * **Tìm kiếm**
   * `B` [Tìm kiếm tuyến tính](src/algorithms/search/linear-search)
-  * `B` [Tìm kiếm nhảy](src/algorithms/search/jump-search) (tìm khối) - tìm kiếm trong mảng đã sắp xếp 
-  * `B` [Tìm kiếm nhị phân](src/algorithms/search/binary-search) - tìm kiếm trong mảng đã sắp xếp 
+  * `B` [Tìm kiếm nhảy](src/algorithms/search/jump-search) (tìm khối) - tìm kiếm trong mảng đã sắp xếp
+  * `B` [Tìm kiếm nhị phân](src/algorithms/search/binary-search) - tìm kiếm trong mảng đã sắp xếp
   * `B` [Tìm kiếm nội suy ](src/algorithms/search/interpolation-search) - Tìm kiếm strong mảng có thứ tự được phân phối đồng nhất
 * **Sắp xếp**
   * `B` [Sắp xếp nổi bọt](src/algorithms/sorting/bubble-sort)
@@ -144,12 +145,12 @@ quy tắc xác định chính xác một chuỗi phép toán.
   * `A` [Các thành phần kết nối chặt](src/algorithms/graph/strongly-connected-components) - Thuật toán Kosaraju
   * `A` [Bài toán người bán hàng](src/algorithms/graph/travelling-salesman) - tuyến đường ngắn nhất có thể đến thăm từng thành phố và trở về thành phố gốc
 * **Mật mã học**
-  * `B` [Băm đa thức](src/algorithms/cryptography/polynomial-hash) - lăn hàm băm dựa trên đa thức 
-  * `B` [Mật mã hàng rào đường sắt](src/algorithms/cryptography/rail-fence-cipher) - một thuật toán mật mã chuyển vị để mã hóa thông điệp 
+  * `B` [Băm đa thức](src/algorithms/cryptography/polynomial-hash) - lăn hàm băm dựa trên đa thức
+  * `B` [Mật mã hàng rào đường sắt](src/algorithms/cryptography/rail-fence-cipher) - một thuật toán mật mã chuyển vị để mã hóa thông điệp
   * `B` [Mật mã Caesar](src/algorithms/cryptography/caesar-cipher) - mật mã chuyển vị đơn giản
   * `B` [Mật mã Hill](src/algorithms/cryptography/hill-cipher) - mật mã chuyển vị đơn giản dựa trên đại số tuyến tính
 * **Học máy**
-  * `B` [NanoNeuron](https://github.com/trekhleb/nano-neuron) - 7 hàm JS đơn giản minh họa cách máy tính thực sự có thể học (truyền thuận / truyền ngược) 
+  * `B` [NanoNeuron](https://github.com/trekhleb/nano-neuron) - 7 hàm JS đơn giản minh họa cách máy tính thực sự có thể học (truyền thuận / truyền ngược)
   * `B` [k-NN](src/algorithms/ml/knn) - thuật toán phân loại k láng giềng gần nhất
   * `B` [k-Means](src/algorithms/ml/k-means) - thuật toán phân cụm k-Means
 * **Khác**
@@ -167,22 +168,22 @@ quy tắc xác định chính xác một chuỗi phép toán.
 
 Mẫu hình thuật toán là một phương pháp hoặc cách tiếp cận chung làm cơ sở cho việc thiết kế một
 lớp thuật toán. Nó là một sự trừu tượng cao hơn khái niệm về một thuật toán, cũng giống như
-một thuật toán là một sự trừu tượng cao hơn một chương trình máy tính. 
+một thuật toán là một sự trừu tượng cao hơn một chương trình máy tính.
 
-* **Vét cạn** - xem xét tất cả các khả năng và chọn giải pháp tốt nhất 
+* **Vét cạn** - xem xét tất cả các khả năng và chọn giải pháp tốt nhất
   * `B` [Tìm kiếm tuyến tính](src/algorithms/search/linear-search)
   * `B` [Thu thập nước mưa](src/algorithms/uncategorized/rain-terraces) - bài toán bẫy nước mưa
   * `B` [Cầu thang đệ quy](src/algorithms/uncategorized/recursive-staircase) - đếm số cách lên đến đỉnh
   * `A` [Mảng con lớn nhất](src/algorithms/sets/maximum-subarray)
   * `A` [Bài toán người bán hàng](src/algorithms/graph/travelling-salesman) - tuyến đường ngắn nhất có thể đến thăm từng thành phố và trở về thành phố gốc
   * `A` [Biến đổi Fourier rời rạc](src/algorithms/math/fourier-transform) - phân giải tín hiệu thời gian thành các tần số tạo nên tín hiệu đó
-* **Tham lam** - chọn phương án tốt nhất vào thời điểm hiện tại mà không cần cân nhắc đến tương lai 
+* **Tham lam** - chọn phương án tốt nhất vào thời điểm hiện tại mà không cần cân nhắc đến tương lai
   * `B` [Trò chơi nhảy](src/algorithms/uncategorized/jump-game)
   * `A` [Bài xếp ba lô không bị chặn](src/algorithms/sets/knapsack-problem)
   * `A` [Thuật toán Dijkstra](src/algorithms/graph/dijkstra) - tìm những đường ngắn nhất từ một định tới tất cả các đỉnh
   * `A` [Thuật toán Prim](src/algorithms/graph/prim) - tìm cây bao trùm nhỏ nhất (MST) cho đồ thị vô hướng có trọng số
   * `A` [Thuật toán Kruskal](src/algorithms/graph/kruskal) - tìm cây bao trùm nhỏ nhất (MST) cho đồ thị vô hướng có trọng số
-* **Chia để trị** - chia vấn đề thành các phần nhỏ hơn rồi giải quyết các phần đó 
+* **Chia để trị** - chia vấn đề thành các phần nhỏ hơn rồi giải quyết các phần đó
   * `B` [Tìm kiếm nhị phân](src/algorithms/search/binary-search)
   * `B` [Tháp Hà Nội](src/algorithms/uncategorized/hanoi-tower)
   * `B` [Tam giác Pascal](src/algorithms/math/pascal-triangle)
@@ -191,7 +192,7 @@ một thuật toán là một sự trừu tượng cao hơn một chương trìn
   * `B` [Sắp xếp nhanh](src/algorithms/sorting/quick-sort)
   * `B` [Cây tìm kiếm theo chiều sâu](src/algorithms/tree/depth-first-search) (DFS)
   * `B` [Đồ thị tìm kiếm theo chiều sâu](src/algorithms/graph/depth-first-search) (DFS)
-  * `B` [Ma trận](src/algorithms/math/matrix) - tạo và duyệt các ma trận có kích thước khác nhau 
+  * `B` [Ma trận](src/algorithms/math/matrix) - tạo và duyệt các ma trận có kích thước khác nhau
   * `B` [Trò chơi nhảy](src/algorithms/uncategorized/jump-game)
   * `B` [Tính nhanh lũy thừa](src/algorithms/math/fast-powering)
   * `B` [Thời điểm tốt nhất để mua bán cổ phiếu](src/algorithms/uncategorized/best-time-to-buy-sell-stocks) - ví dụ chia để trị và một đường chuyền
@@ -216,18 +217,18 @@ một thuật toán là một sự trừu tượng cao hơn một chương trìn
   * `A` [Phối biểu thức chính quy](src/algorithms/string/regular-expression-matching)
 * **Quay lui** - tương tự như vét cạn, cố tạo ra tất cả các giải pháp có thể, nhưng mỗi lần bạn tạo ra giải pháp tiếp theo,
 bạn sẽ kiểm tra xem nó có thỏa mãn tất cả các điều kiện hay không và chỉ khi thỏa mãn mới tiếp tục tạo ra các giải pháp tiếp theo.
-Nếu không, hãy quay lại và đi trên một con đường khác để tìm ra giải pháp. Thông thường, truyền DFS của không gian trạng thái được sử dụng. 
+Nếu không, hãy quay lại và đi trên một con đường khác để tìm ra giải pháp. Thông thường, truyền DFS của không gian trạng thái được sử dụng.
   * `B` [Trò chơi nhảy](src/algorithms/uncategorized/jump-game)
   * `B` [Đường đi độc nhất](src/algorithms/uncategorized/unique-paths)
   * `B` [Tập lũy thừa](src/algorithms/sets/power-set) - tập hợp chứa tất cả các tập con
   * `A` [Chu trình Hamilton](src/algorithms/graph/hamiltonian-cycle) - đi qua các đỉnh một lần duy nhất
   * `A` [Bài toán n quân hậu](src/algorithms/uncategorized/n-queens)
   * `A` [Mã đi tuần](src/algorithms/uncategorized/knight-tour)
-  * `A` [Tổ hợp của tổng](src/algorithms/sets/combination-sum) - tìm tất cả các tổ hợp tạo thành tổng cụ thể 
+  * `A` [Tổ hợp của tổng](src/algorithms/sets/combination-sum) - tìm tất cả các tổ hợp tạo thành tổng cụ thể
 * **Branch & Bound** - ghi nhớ giải pháp chi với phí thấp nhất được tìm thấy ở mỗi giai đoạn của quá trình tìm kiếm quay lui,
 sử dụng chi phí của giải pháp có chi phí thấp nhất được tìm thấy cho đến nay như một giới hạn dưới về chi phí của
-một giải pháp ít chi phí nhân cho bài toán, để loại bỏ các giải pháp từng phần với chi phí lớn hơn giải pháp chi phí thấp nhất được tìm thấy cho đến nay. 
-Thông thường BFS duyệt kết hợp với duyệt DFS của cây không gian trạng thái đang được sử dụng. 
+một giải pháp ít chi phí nhân cho bài toán, để loại bỏ các giải pháp từng phần với chi phí lớn hơn giải pháp chi phí thấp nhất được tìm thấy cho đến nay.
+Thông thường BFS duyệt kết hợp với duyệt DFS của cây không gian trạng thái đang được sử dụng.
 
 ## Hướng dẫn sử dụng repository
 
@@ -238,7 +239,7 @@ npm install
 
 **Chạy ESLint**
 
-Bạn có thể muốn chạy nó để kiểm tra chất lượng code. 
+Bạn có thể muốn chạy nó để kiểm tra chất lượng code.
 
 ```
 npm run lint
@@ -259,7 +260,7 @@ npm test -- 'LinkedList'
 Bạn có thể chơi với các cấu trúc dữ liệu và thuật toán trong tệp `./src/playground/playground.js`
 và viết các bài kiểm thử cho nó ở `./src/playground/__test__/playground.test.js`.
 
-Sau đó, chỉ cần chạy lệnh sau để kiểm tra xem sân chơi của bạn có hoạt động như mong đợi hay không: 
+Sau đó, chỉ cần chạy lệnh sau để kiểm tra xem sân chơi của bạn có hoạt động như mong đợi hay không:
 
 ```
 npm test -- 'playground'
@@ -274,7 +275,7 @@ npm test -- 'playground'
 ### Kí hiệu O lớn
 
 *Kí hiệu O lớn* được dùng để phân loại thuật toán theo thời gian chạy hoặc yêu cầu không gian gia tăng khi kích thước đầu vào gia tăng.
-Trên biểu đồ bên dưới, bạn có thể tìm thấy hầu hết các thứ tự tăng trưởng phổ biến của các thuật toán được chỉ định trong ký hiệu O lớn. 
+Trên biểu đồ bên dưới, bạn có thể tìm thấy hầu hết các thứ tự tăng trưởng phổ biến của các thuật toán được chỉ định trong ký hiệu O lớn.
 
 ![Đồ thị O lớn](./assets/big-o-graph.png)
 
diff --git a/README.zh-CN.md b/README.zh-CN.md
index cdef9cabea..14e0f0e36c 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -23,7 +23,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 *注意：这个项目仅用于学习和研究，**不是**用于生产环境。*
 
diff --git a/README.zh-TW.md b/README.zh-TW.md
index 20d4381a2a..aa48b40bd8 100644
--- a/README.zh-TW.md
+++ b/README.zh-TW.md
@@ -22,7 +22,9 @@ _Read this in other languages:_
 [_Українська_](README.uk-UA.md),
 [_Arabic_](README.ar-AR.md),
 [_Tiếng Việt_](README.vi-VN.md),
-[_Deutsch_](README.de-DE.md)
+[_Deutsch_](README.de-DE.md),
+[_Uzbek_](README.uz-UZ.md)
+[_עברית_](README.he-IL.md)
 
 ## 資料結構
 
diff --git a/jest.config.js b/jest.config.js
index 3fec225237..78568a1e94 100644
--- a/jest.config.js
+++ b/jest.config.js
@@ -24,7 +24,9 @@ module.exports = {
   // This option sets the URL for the jsdom environment.
   // It is reflected in properties such as location.href.
   // @see: https://github.com/facebook/jest/issues/6769
-  testURL: 'http://localhost/',
+  testEnvironmentOptions: {
+    url: 'http://localhost/',
+  },
 
   // @see: https://jestjs.io/docs/en/configuration#coveragethreshold-object
   coverageThreshold: {
diff --git a/package-lock.json b/package-lock.json
index a1e4b4f8ca..b61d279d87 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,555 +1,9657 @@
 {
   "name": "javascript-algorithms-and-data-structures",
   "version": "0.0.4",
-  "lockfileVersion": 1,
+  "lockfileVersion": 2,
   "requires": true,
+  "packages": {
+    "": {
+      "name": "javascript-algorithms-and-data-structures",
+      "version": "0.0.4",
+      "license": "MIT",
+      "devDependencies": {
+        "@babel/cli": "7.20.7",
+        "@babel/preset-env": "7.20.2",
+        "@types/jest": "29.4.0",
+        "eslint": "8.33.0",
+        "eslint-config-airbnb": "19.0.4",
+        "eslint-plugin-import": "2.27.5",
+        "eslint-plugin-jest": "27.2.1",
+        "eslint-plugin-jsx-a11y": "6.7.1",
+        "husky": "8.0.3",
+        "jest": "29.4.1",
+        "pngjs": "^7.0.0"
+      },
+      "engines": {
+        "node": ">=16.15.0",
+        "npm": ">=8.5.5"
+      }
+    },
+    "node_modules/@ampproject/remapping": {
+      "version": "2.2.1",
+      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.2.1.tgz",
+      "integrity": "sha512-lFMjJTrFL3j7L9yBxwYfCq2k6qqwHyzuUl/XBnif78PWTJYyL/dfowQHWE3sp6U6ZzqWiiIZnpTMO96zhkjwtg==",
+      "dev": true,
+      "dependencies": {
+        "@jridgewell/gen-mapping": "^0.3.0",
+        "@jridgewell/trace-mapping": "^0.3.9"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@babel/cli": {
+      "version": "7.20.7",
+      "resolved": "https://registry.npmjs.org/@babel/cli/-/cli-7.20.7.tgz",
+      "integrity": "sha512-WylgcELHB66WwQqItxNILsMlaTd8/SO6SgTTjMp4uCI7P4QyH1r3nqgFmO3BfM4AtfniHgFMH3EpYFj/zynBkQ==",
+      "dev": true,
+      "dependencies": {
+        "@jridgewell/trace-mapping": "^0.3.8",
+        "commander": "^4.0.1",
+        "convert-source-map": "^1.1.0",
+        "fs-readdir-recursive": "^1.1.0",
+        "glob": "^7.2.0",
+        "make-dir": "^2.1.0",
+        "slash": "^2.0.0"
+      },
+      "bin": {
+        "babel": "bin/babel.js",
+        "babel-external-helpers": "bin/babel-external-helpers.js"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "optionalDependencies": {
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+        "chokidar": "^3.4.0"
+      },
+      "peerDependencies": {
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+      "dev": true,
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+      }
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+        "@babel/types": "^7.21.4",
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+        "json5": "^2.2.2",
+        "semver": "^6.3.0"
+      },
+      "engines": {
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+      },
+      "funding": {
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+      }
+    },
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-      "integrity": "sha512-AvHcyZ5JnSfq3ioSyjrBkH9yW4m7Ayk8/9My/DD9onKeu/94fwrMocemO2QAJFAlnnDN+ZDS+ZjAR5ua1/PV/Q==",
+      "version": "4.0.2",
+      "resolved": "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-4.0.2.tgz",
+      "integrity": "sha512-7KxauUdBmSdWnmpaGFg+ppNjKF8uNLry8LyzjauQDOVONfFLNKrKvQOxZ/VuTIcS/gge/YNahf5RIIQWTSarlg==",
       "dev": true,
       "requires": {
         "imurmurhash": "^0.1.4",
-        "is-typedarray": "^1.0.0",
-        "signal-exit": "^3.0.2",
-        "typedarray-to-buffer": "^3.1.5"
+        "signal-exit": "^3.0.7"
       }
     },
-    "ws": {
-      "version": "7.5.6",
-      "resolved": "https://registry.npmjs.org/ws/-/ws-7.5.6.tgz",
-      "integrity": "sha512-6GLgCqo2cy2A2rjCNFlxQS6ZljG/coZfZXclldI8FB/1G3CCI36Zd8xy2HrFVACi8tfk5XrgLQEk+P0Tnz9UcA==",
-      "dev": true
-    },
-    "xml-name-validator": {
-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-3.0.0.tgz",
-      "integrity": "sha512-A5CUptxDsvxKJEU3yO6DuWBSJz/qizqzJKOMIfUJHETbBw/sFaDxgd6fxm1ewUaM0jZ444Fc5vC5ROYurg/4Pw==",
-      "dev": true
-    },
-    "xmlchars": {
-      "version": "2.2.0",
-      "resolved": "https://registry.npmjs.org/xmlchars/-/xmlchars-2.2.0.tgz",
-      "integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==",
-      "dev": true
-    },
     "y18n": {
       "version": "5.0.8",
       "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
@@ -7128,30 +15752,36 @@
       "dev": true
     },
     "yallist": {
-      "version": "4.0.0",
-      "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
-      "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==",
+      "version": "3.1.1",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+      "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==",
       "dev": true
     },
     "yargs": {
-      "version": "16.2.0",
-      "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
-      "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
+      "version": "17.7.1",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.1.tgz",
+      "integrity": "sha512-cwiTb08Xuv5fqF4AovYacTFNxk62th7LKJ6BL9IGUpTJrWoU7/7WdQGTP2SjKf1dUNBGzDd28p/Yfs/GI6JrLw==",
       "dev": true,
       "requires": {
-        "cliui": "^7.0.2",
+        "cliui": "^8.0.1",
         "escalade": "^3.1.1",
         "get-caller-file": "^2.0.5",
         "require-directory": "^2.1.1",
-        "string-width": "^4.2.0",
+        "string-width": "^4.2.3",
         "y18n": "^5.0.5",
-        "yargs-parser": "^20.2.2"
+        "yargs-parser": "^21.1.1"
       }
     },
     "yargs-parser": {
-      "version": "20.2.9",
-      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz",
-      "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==",
+      "version": "21.1.1",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
+      "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
+      "dev": true
+    },
+    "yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
       "dev": true
     }
   }
diff --git a/package.json b/package.json
index 6d29c89e11..95d6d28942 100644
--- a/package.json
+++ b/package.json
@@ -35,21 +35,20 @@
     "prepare": "husky install"
   },
   "devDependencies": {
-    "@babel/cli": "7.16.8",
-    "@babel/preset-env": "7.16.11",
-    "@types/jest": "27.4.0",
-    "canvas": "2.9.0",
-    "eslint": "8.7.0",
+    "@babel/cli": "7.20.7",
+    "@babel/preset-env": "7.20.2",
+    "@types/jest": "29.4.0",
+    "eslint": "8.33.0",
     "eslint-config-airbnb": "19.0.4",
-    "eslint-plugin-import": "2.25.4",
-    "eslint-plugin-jest": "25.7.0",
-    "eslint-plugin-jsx-a11y": "6.5.1",
-    "eslint-plugin-react": "7.28.0",
-    "husky": "7.0.4",
-    "jest": "27.4.7"
+    "eslint-plugin-import": "2.27.5",
+    "eslint-plugin-jest": "27.2.1",
+    "eslint-plugin-jsx-a11y": "6.7.1",
+    "husky": "8.0.3",
+    "jest": "29.4.1",
+    "pngjs": "^7.0.0"
   },
   "engines": {
-    "node": ">=14.16.0",
-    "npm": ">=6.14.0"
+    "node": ">=16.15.0",
+    "npm": ">=8.5.5"
   }
 }
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.node.js b/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.node.js
new file mode 100644
index 0000000000..e4f78b7a52
--- /dev/null
+++ b/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.node.js
@@ -0,0 +1,85 @@
+import fs from 'fs';
+import { PNG } from 'pngjs';
+
+import resizeImageWidth from '../resizeImageWidth';
+
+const testImageBeforePath = './src/algorithms/image-processing/seam-carving/__tests__/test-image-before.png';
+const testImageAfterPath = './src/algorithms/image-processing/seam-carving/__tests__/test-image-after.png';
+
+/**
+ * Compares two images and finds the number of different pixels.
+ *
+ * @param {ImageData} imgA - ImageData for the first image.
+ * @param {ImageData} imgB - ImageData for the second image.
+ * @param {number} threshold - Color difference threshold [0..255]. Smaller - stricter.
+ * @returns {number} - Number of different pixels.
+ */
+function pixelsDiff(imgA, imgB, threshold = 0) {
+  if (imgA.width !== imgB.width || imgA.height !== imgB.height) {
+    throw new Error('Images must have the same size');
+  }
+
+  let differentPixels = 0;
+  const numColorParams = 4; // RGBA
+
+  for (let pixelIndex = 0; pixelIndex < imgA.data.length; pixelIndex += numColorParams) {
+    // Get pixel's color for each image.
+    const [aR, aG, aB] = imgA.data.subarray(pixelIndex, pixelIndex + numColorParams);
+    const [bR, bG, bB] = imgB.data.subarray(pixelIndex, pixelIndex + numColorParams);
+
+    // Get average pixel's color for each image (make them greyscale).
+    const aAvgColor = Math.floor((aR + aG + aB) / 3);
+    const bAvgColor = Math.floor((bR + bG + bB) / 3);
+
+    // Compare pixel colors.
+    if (Math.abs(aAvgColor - bAvgColor) > threshold) {
+      differentPixels += 1;
+    }
+  }
+
+  return differentPixels;
+}
+
+const pngLoad = (path) => new Promise((resolve) => {
+  fs.createReadStream(path)
+    .pipe(new PNG())
+    .on('parsed', function Parsed() {
+      /** @type {ImageData} */
+      const imageData = {
+        colorSpace: 'srgb',
+        width: this.width,
+        height: this.height,
+        data: this.data,
+      };
+      resolve(imageData);
+    });
+});
+
+describe('resizeImageWidth', () => {
+  it('should perform content-aware image width reduction', async () => {
+    const imgBefore = await pngLoad(testImageBeforePath);
+    const imgAfter = await pngLoad(testImageAfterPath);
+
+    const toWidth = Math.floor(imgBefore.width / 2);
+
+    const {
+      img: imgResized,
+      size: resizedSize,
+    } = resizeImageWidth({ img: imgBefore, toWidth });
+
+    expect(imgResized).toBeDefined();
+    expect(resizedSize).toBeDefined();
+
+    expect(resizedSize).toEqual({ w: toWidth, h: imgBefore.height });
+    expect(imgResized.width).toBe(imgAfter.width);
+    expect(imgResized.height).toBe(imgAfter.height);
+
+    const colorThreshold = 50;
+    const differentPixels = pixelsDiff(imgResized, imgAfter, colorThreshold);
+
+    // Allow 10% of pixels to be different
+    const pixelsThreshold = Math.floor((imgAfter.width * imgAfter.height) / 10);
+
+    expect(differentPixels).toBeLessThanOrEqual(pixelsThreshold);
+  });
+});
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.test.js b/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.test.js
deleted file mode 100644
index 9afed97123..0000000000
--- a/src/algorithms/image-processing/seam-carving/__tests__/resizeImageWidth.test.js
+++ /dev/null
@@ -1,91 +0,0 @@
-import { createCanvas, loadImage } from 'canvas';
-import resizeImageWidth from '../resizeImageWidth';
-
-const testImageBeforePath = './src/algorithms/image-processing/seam-carving/__tests__/test-image-before.jpg';
-const testImageAfterPath = './src/algorithms/image-processing/seam-carving/__tests__/test-image-after.jpg';
-
-/**
- * Compares two images and finds the number of different pixels.
- *
- * @param {ImageData} imgA - ImageData for the first image.
- * @param {ImageData} imgB - ImageData for the second image.
- * @param {number} threshold - Color difference threshold [0..255]. Smaller - stricter.
- * @returns {number} - Number of different pixels.
- */
-function pixelsDiff(imgA, imgB, threshold = 0) {
-  if (imgA.width !== imgB.width || imgA.height !== imgB.height) {
-    throw new Error('Images must have the same size');
-  }
-
-  let differentPixels = 0;
-  const numColorParams = 4; // RGBA
-
-  for (let pixelIndex = 0; pixelIndex < imgA.data.length; pixelIndex += numColorParams) {
-    // Get pixel's color for each image.
-    const [aR, aG, aB] = imgA.data.subarray(pixelIndex, pixelIndex + numColorParams);
-    const [bR, bG, bB] = imgB.data.subarray(pixelIndex, pixelIndex + numColorParams);
-
-    // Get average pixel's color for each image (make them greyscale).
-    const aAvgColor = Math.floor((aR + aG + aB) / 3);
-    const bAvgColor = Math.floor((bR + bG + bB) / 3);
-
-    // Compare pixel colors.
-    if (Math.abs(aAvgColor - bAvgColor) > threshold) {
-      differentPixels += 1;
-    }
-  }
-
-  return differentPixels;
-}
-
-describe('resizeImageWidth', () => {
-  it('should perform content-aware image width reduction', () => {
-    // @see: https://jestjs.io/docs/asynchronous
-    return Promise.all([
-      loadImage(testImageBeforePath),
-      loadImage(testImageAfterPath),
-    ]).then(([imgBefore, imgAfter]) => {
-      // Original image.
-      const canvasBefore = createCanvas(imgBefore.width, imgBefore.height);
-      const ctxBefore = canvasBefore.getContext('2d');
-      ctxBefore.drawImage(imgBefore, 0, 0, imgBefore.width, imgBefore.height);
-      const imgDataBefore = ctxBefore.getImageData(0, 0, imgBefore.width, imgBefore.height);
-
-      // Resized image saved.
-      const canvasAfter = createCanvas(imgAfter.width, imgAfter.height);
-      const ctxAfter = canvasAfter.getContext('2d');
-      ctxAfter.drawImage(imgAfter, 0, 0, imgAfter.width, imgAfter.height);
-      const imgDataAfter = ctxAfter.getImageData(0, 0, imgAfter.width, imgAfter.height);
-
-      const toWidth = Math.floor(imgBefore.width / 2);
-
-      const {
-        img: resizedImg,
-        size: resizedSize,
-      } = resizeImageWidth({ img: imgDataBefore, toWidth });
-
-      expect(resizedImg).toBeDefined();
-      expect(resizedSize).toBeDefined();
-
-      // Resized image generated.
-      const canvasTest = createCanvas(resizedSize.w, resizedSize.h);
-      const ctxTest = canvasTest.getContext('2d');
-      ctxTest.putImageData(resizedImg, 0, 0, 0, 0, resizedSize.w, resizedSize.h);
-      const imgDataTest = ctxTest.getImageData(0, 0, resizedSize.w, resizedSize.h);
-
-      expect(resizedSize).toEqual({ w: toWidth, h: imgBefore.height });
-      expect(imgDataTest.width).toBe(toWidth);
-      expect(imgDataTest.height).toBe(imgBefore.height);
-      expect(imgDataTest.width).toBe(imgAfter.width);
-      expect(imgDataTest.height).toBe(imgAfter.height);
-
-      const colorThreshold = 50;
-      const differentPixels = pixelsDiff(imgDataTest, imgDataAfter, colorThreshold);
-
-      // Allow 10% of pixels to be different
-      const pixelsThreshold = Math.floor((imgAfter.width * imgAfter.height) / 10);
-
-      expect(differentPixels).toBeLessThanOrEqual(pixelsThreshold);
-    });
-  });
-});
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.jpg b/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.jpg
deleted file mode 100644
index 9af094e3ea..0000000000
Binary files a/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.jpg and /dev/null differ
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.png b/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.png
new file mode 100644
index 0000000000..8b5724cd30
Binary files /dev/null and b/src/algorithms/image-processing/seam-carving/__tests__/test-image-after.png differ
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.jpg b/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.jpg
deleted file mode 100644
index 2e34461e4b..0000000000
Binary files a/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.jpg and /dev/null differ
diff --git a/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.png b/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.png
new file mode 100644
index 0000000000..20a53207dd
Binary files /dev/null and b/src/algorithms/image-processing/seam-carving/__tests__/test-image-before.png differ
diff --git a/src/algorithms/math/factorial/README.md b/src/algorithms/math/factorial/README.md
index 5f7f0e1129..9c75b41aae 100644
--- a/src/algorithms/math/factorial/README.md
+++ b/src/algorithms/math/factorial/README.md
@@ -1,7 +1,7 @@
 # Factorial
 
 _Read this in other languages:_
-[_简体中文_](README.zh-CN.md), [français](README.fr-FR.md), [turkish](README.tr-TR.md), [ქართული](README.ka-GE.md).
+[_简体中文_](README.zh-CN.md), [_Français_](README.fr-FR.md), [_Türkçe_](README.tr-TR.md), [_ქართული_](README.ka-GE.md), [_Українська_](README.uk-UA.md).
 
 In mathematics, the factorial of a non-negative integer `n`,
 denoted by `n!`, is the product of all positive integers less
diff --git a/src/algorithms/math/factorial/README.uk-UA.md b/src/algorithms/math/factorial/README.uk-UA.md
new file mode 100644
index 0000000000..bf75963113
--- /dev/null
+++ b/src/algorithms/math/factorial/README.uk-UA.md
@@ -0,0 +1,33 @@
+# Факторіал
+
+_Прочитайте це іншими мовами:_
+[_English_](README.md), [_简体中文_](README.zh-CN.md), [_Français_](README.fr-FR.md), [_Türkçe_](README.tr-TR.md), [_ქართული_](README.ka-GE.md).
+
+У математиці факторіал невід'ємного цілого числа `n`, позначений `n!`, є добутком усіх натуральних чисел, менших або рівних `n`. Наприклад:
+
+```
+5! = 5 * 4 * 3 * 2 * 1 = 120
+```
+
+| n   |                n! |
+| --- | ----------------: |
+| 0   |                 1 |
+| 1   |                 1 |
+| 2   |                 2 |
+| 3   |                 6 |
+| 4   |                24 |
+| 5   |               120 |
+| 6   |               720 |
+| 7   |             5 040 |
+| 8   |            40 320 |
+| 9   |           362 880 |
+| 10  |         3 628 800 |
+| 11  |        39 916 800 |
+| 12  |       479 001 600 |
+| 13  |     6 227 020 800 |
+| 14  |    87 178 291 200 |
+| 15  | 1 307 674 368 000 |
+
+## Посилання
+
+[Wikipedia](https://uk.wikipedia.org/wiki/%D0%A4%D0%B0%D0%BA%D1%82%D0%BE%D1%80%D1%96%D0%B0%D0%BB)
diff --git a/src/algorithms/ml/k-means/README.md b/src/algorithms/ml/k-means/README.md
index 4221880054..fb58c1bfa4 100644
--- a/src/algorithms/ml/k-means/README.md
+++ b/src/algorithms/ml/k-means/README.md
@@ -30,6 +30,11 @@ _Image source: [Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering)_
 
 The centroids are moving continuously in order to create better distinction between the different set of data points. As we can see, after a few iterations, the difference in centroids is quite low between iterations. For example between iterations `13` and `14` the difference is quite small because there the optimizer is tuning boundary cases.
 
+## Code Examples
+
+- [kMeans.js](./kMeans.js)
+- [kMeans.test.js](./__test__/kMeans.test.js) (test cases)
+
 ## References
 
 - [k-Means neighbors algorithm on Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering)
diff --git a/src/algorithms/search/binary-search/README.es-ES.md b/src/algorithms/search/binary-search/README.es-ES.md
new file mode 100644
index 0000000000..f14aef985f
--- /dev/null
+++ b/src/algorithms/search/binary-search/README.es-ES.md
@@ -0,0 +1,27 @@
+# Búsqueda binaria
+
+_Lea esto en otros idiomas:_
+[English](README.md)
+[Português brasileiro](README.pt-BR.md).
+
+En informática, la búsqueda binaria, también conocida como búsqueda de medio intervalo
+búsqueda, búsqueda logarítmica, o corte binario, es un algoritmo de búsqueda
+que encuentra la posición de un valor objetivo dentro de una matriz
+ordenada. La búsqueda binaria compara el valor objetivo con el elemento central
+de la matriz; si son desiguales, se elimina la mitad en la que
+la mitad en la que no puede estar el objetivo se elimina y la búsqueda continúa
+en la mitad restante hasta que tenga éxito. Si la búsqueda
+termina con la mitad restante vacía, el objetivo no está
+en la matriz.
+
+![Búsqueda binaria](https://upload.wikimedia.org/wikipedia/commons/8/83/Binary_Search_Depiction.svg)
+
+## Complejidad
+
+**Complejidad de tiempo**: `O(log(n))` - ya que dividimos el área de búsqueda en dos para cada
+siguiente iteración.
+
+## Referencias
+
+- [Wikipedia](https://en.wikipedia.org/wiki/Binary_search_algorithm)
+- [YouTube](https://www.youtube.com/watch?v=P3YID7liBug&index=29&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
diff --git a/src/algorithms/search/binary-search/README.md b/src/algorithms/search/binary-search/README.md
index ebf3e123e0..34d1bd7a40 100644
--- a/src/algorithms/search/binary-search/README.md
+++ b/src/algorithms/search/binary-search/README.md
@@ -2,15 +2,16 @@
 
 _Read this in other languages:_
 [Português brasileiro](README.pt-BR.md).
+[Español](README.es-ES.md).
 
-In computer science, binary search, also known as half-interval 
-search, logarithmic search, or binary chop, is a search algorithm 
-that finds the position of a target value within a sorted 
-array. Binary search compares the target value to the middle 
-element of the array; if they are unequal, the half in which 
-the target cannot lie is eliminated and the search continues 
-on the remaining half until it is successful. If the search 
-ends with the remaining half being empty, the target is not 
+In computer science, binary search, also known as half-interval
+search, logarithmic search, or binary chop, is a search algorithm
+that finds the position of a target value within a sorted
+array. Binary search compares the target value to the middle
+element of the array; if they are unequal, the half in which
+the target cannot lie is eliminated and the search continues
+on the remaining half until it is successful. If the search
+ends with the remaining half being empty, the target is not
 in the array.
 
 ![Binary Search](https://upload.wikimedia.org/wikipedia/commons/8/83/Binary_Search_Depiction.svg)
diff --git a/src/algorithms/search/binary-search/README.pt-BR.md b/src/algorithms/search/binary-search/README.pt-BR.md
index 14445a048e..4f9de9f199 100644
--- a/src/algorithms/search/binary-search/README.pt-BR.md
+++ b/src/algorithms/search/binary-search/README.pt-BR.md
@@ -2,6 +2,7 @@
 
 _Leia isso em outras línguas:_
 [english](README.md).
+[Español](README.es-ES.md).
 
 Em ciência da computação, busca binária, também conhecida como busca de meio-intervalo, busca logarítmica ou corte binário, é um algoritmo de pesquisa
 que encontra a posição de um elemento alvo dentro de um
diff --git a/src/algorithms/search/binary-search/binarySearch.js b/src/algorithms/search/binary-search/binarySearch.js
index b9e18aab7b..5c8d48fbea 100644
--- a/src/algorithms/search/binary-search/binarySearch.js
+++ b/src/algorithms/search/binary-search/binarySearch.js
@@ -11,7 +11,7 @@ import Comparator from '../../../utils/comparator/Comparator';
 
 export default function binarySearch(sortedArray, seekElement, comparatorCallback) {
   // Let's create comparator from the comparatorCallback function.
-  // Comparator object will give us common comparison methods like equal() and lessThen().
+  // Comparator object will give us common comparison methods like equal() and lessThan().
   const comparator = new Comparator(comparatorCallback);
 
   // These two indices will contain current array (sub-array) boundaries.
diff --git a/src/algorithms/sets/longest-common-subsequence/__test__/longestCommonSubsequenceRecursive.test.js b/src/algorithms/sets/longest-common-subsequence/__test__/longestCommonSubsequenceRecursive.test.js
new file mode 100644
index 0000000000..3d5c9d802a
--- /dev/null
+++ b/src/algorithms/sets/longest-common-subsequence/__test__/longestCommonSubsequenceRecursive.test.js
@@ -0,0 +1,17 @@
+import longestCommonSubsequence from '../longestCommonSubsequenceRecursive';
+
+describe('longestCommonSubsequenceRecursive', () => {
+  it('should find longest common substring between two strings', () => {
+    expect(longestCommonSubsequence('', '')).toBe('');
+    expect(longestCommonSubsequence('ABC', '')).toBe('');
+    expect(longestCommonSubsequence('', 'ABC')).toBe('');
+    expect(longestCommonSubsequence('ABABC', 'BABCA')).toBe('BABC');
+    expect(longestCommonSubsequence('BABCA', 'ABCBA')).toBe('ABCA');
+    expect(longestCommonSubsequence('sea', 'eat')).toBe('ea');
+    expect(longestCommonSubsequence('algorithms', 'rithm')).toBe('rithm');
+    expect(longestCommonSubsequence(
+      'Algorithms and data structures implemented in JavaScript',
+      'Here you may find Algorithms and data structures that are implemented in JavaScript',
+    )).toBe('Algorithms and data structures implemented in JavaScript');
+  });
+});
diff --git a/src/algorithms/sets/longest-common-subsequence/longestCommonSubsequenceRecursive.js b/src/algorithms/sets/longest-common-subsequence/longestCommonSubsequenceRecursive.js
new file mode 100644
index 0000000000..580ba8c72f
--- /dev/null
+++ b/src/algorithms/sets/longest-common-subsequence/longestCommonSubsequenceRecursive.js
@@ -0,0 +1,36 @@
+/* eslint-disable no-param-reassign */
+/**
+ * Longest Common Subsequence (LCS) (Recursive Approach).
+ *
+ * @param {string} string1
+ * @param {string} string2
+ * @return {number}
+ */
+export default function longestCommonSubsequenceRecursive(string1, string2) {
+  /**
+   *
+   * @param {string} s1
+   * @param {string} s2
+   * @return {string} - returns the LCS (Longest Common Subsequence)
+   */
+  const lcs = (s1, s2, memo = {}) => {
+    if (!s1 || !s2) return '';
+
+    if (memo[`${s1}:${s2}`]) return memo[`${s1}:${s2}`];
+
+    if (s1[0] === s2[0]) {
+      return s1[0] + lcs(s1.substring(1), s2.substring(1), memo);
+    }
+
+    const nextLcs1 = lcs(s1.substring(1), s2, memo);
+    const nextLcs2 = lcs(s1, s2.substring(1), memo);
+
+    const nextLongest = nextLcs1.length >= nextLcs2.length ? nextLcs1 : nextLcs2;
+
+    memo[`${s1}:${s2}`] = nextLongest;
+
+    return nextLongest;
+  };
+
+  return lcs(string1, string2);
+}
diff --git a/src/algorithms/sorting/bucket-sort/BucketSort.js b/src/algorithms/sorting/bucket-sort/BucketSort.js
new file mode 100644
index 0000000000..a41e5892d2
--- /dev/null
+++ b/src/algorithms/sorting/bucket-sort/BucketSort.js
@@ -0,0 +1,46 @@
+import RadixSort from '../radix-sort/RadixSort';
+
+/**
+ * Bucket Sort
+ *
+ * @param {number[]} arr
+ * @param {number} bucketsNum
+ * @return {number[]}
+ */
+export default function BucketSort(arr, bucketsNum = 1) {
+  const buckets = new Array(bucketsNum).fill(null).map(() => []);
+
+  const minValue = Math.min(...arr);
+  const maxValue = Math.max(...arr);
+
+  const bucketSize = Math.ceil(Math.max(1, (maxValue - minValue) / bucketsNum));
+
+  // Place elements into buckets.
+  for (let i = 0; i < arr.length; i += 1) {
+    const currValue = arr[i];
+    const bucketIndex = Math.floor((currValue - minValue) / bucketSize);
+
+    // Edge case for max value.
+    if (bucketIndex === bucketsNum) {
+      buckets[bucketsNum - 1].push(currValue);
+    } else {
+      buckets[bucketIndex].push(currValue);
+    }
+  }
+
+  // Sort individual buckets.
+  for (let i = 0; i < buckets.length; i += 1) {
+    // Let's use the Radix Sorter here. This may give us
+    // the average O(n + k) time complexity to sort one bucket
+    // (where k is a number of digits in the longest number).
+    buckets[i] = new RadixSort().sort(buckets[i]);
+  }
+
+  // Merge sorted buckets into final output.
+  const sortedArr = [];
+  for (let i = 0; i < buckets.length; i += 1) {
+    sortedArr.push(...buckets[i]);
+  }
+
+  return sortedArr;
+}
diff --git a/src/algorithms/sorting/bucket-sort/README.md b/src/algorithms/sorting/bucket-sort/README.md
new file mode 100644
index 0000000000..673ba80b71
--- /dev/null
+++ b/src/algorithms/sorting/bucket-sort/README.md
@@ -0,0 +1,35 @@
+# Bucket Sort
+
+**Bucket sort**, or **bin sort**, is a sorting algorithm that works by distributing the elements of an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sorting algorithm.
+
+## Algorithm
+
+Bucket sort works as follows:
+
+1. Set up an array of initially empty `buckets`.
+2. **Scatter:** Go over the original array, putting each object in its `bucket`.
+3. Sort each non-empty `bucket`.
+4. **Gather:** Visit the `buckets` in order and put all elements back into the original array.
+
+Elements are distributed among bins:
+
+![Elements are distributed among bins](./images/bucket_sort_1.png)
+
+Then, elements are sorted within each bin:
+
+![Elements are sorted within each bin](./images/bucket_sort_2.png)
+
+
+## Complexity
+
+The computational complexity depends on the algorithm used to sort each bucket, the number of buckets to use, and whether the input is uniformly distributed.
+
+The **worst-case** time complexity of bucket sort is
+`O(n^2)` if the sorting algorithm used on the bucket is *insertion sort*, which is the most common use case since the expectation is that buckets will not have too many elements relative to the entire list. In the worst case, all elements are placed in one bucket, causing the running time to reduce to the worst-case complexity of insertion sort (all elements are in reverse order). If the worst-case running time of the intermediate sort used is `O(n * log(n))`, then the worst-case running time of bucket sort will also be
+`O(n * log(n))`.
+
+On **average**, when the distribution of elements across buckets is reasonably uniform, it can be shown that bucket sort runs on average `O(n + k)` for `k` buckets.
+
+## References
+
+- [Bucket Sort on Wikipedia](https://en.wikipedia.org/wiki/Bucket_sort)
diff --git a/src/algorithms/sorting/bucket-sort/__test__/BucketSort.test.js b/src/algorithms/sorting/bucket-sort/__test__/BucketSort.test.js
new file mode 100644
index 0000000000..9cab525d9b
--- /dev/null
+++ b/src/algorithms/sorting/bucket-sort/__test__/BucketSort.test.js
@@ -0,0 +1,33 @@
+import BucketSort from '../BucketSort';
+import {
+  equalArr,
+  notSortedArr,
+  reverseArr,
+  sortedArr,
+} from '../../SortTester';
+
+describe('BucketSort', () => {
+  it('should sort the array of numbers with different buckets amounts', () => {
+    expect(BucketSort(notSortedArr, 4)).toEqual(sortedArr);
+    expect(BucketSort(equalArr, 4)).toEqual(equalArr);
+    expect(BucketSort(reverseArr, 4)).toEqual(sortedArr);
+    expect(BucketSort(sortedArr, 4)).toEqual(sortedArr);
+
+    expect(BucketSort(notSortedArr, 10)).toEqual(sortedArr);
+    expect(BucketSort(equalArr, 10)).toEqual(equalArr);
+    expect(BucketSort(reverseArr, 10)).toEqual(sortedArr);
+    expect(BucketSort(sortedArr, 10)).toEqual(sortedArr);
+
+    expect(BucketSort(notSortedArr, 50)).toEqual(sortedArr);
+    expect(BucketSort(equalArr, 50)).toEqual(equalArr);
+    expect(BucketSort(reverseArr, 50)).toEqual(sortedArr);
+    expect(BucketSort(sortedArr, 50)).toEqual(sortedArr);
+  });
+
+  it('should sort the array of numbers with the default buckets of 1', () => {
+    expect(BucketSort(notSortedArr)).toEqual(sortedArr);
+    expect(BucketSort(equalArr)).toEqual(equalArr);
+    expect(BucketSort(reverseArr)).toEqual(sortedArr);
+    expect(BucketSort(sortedArr)).toEqual(sortedArr);
+  });
+});
diff --git a/src/algorithms/sorting/bucket-sort/images/bucket_sort_1.png b/src/algorithms/sorting/bucket-sort/images/bucket_sort_1.png
new file mode 100644
index 0000000000..da0de9172f
Binary files /dev/null and b/src/algorithms/sorting/bucket-sort/images/bucket_sort_1.png differ
diff --git a/src/algorithms/sorting/bucket-sort/images/bucket_sort_2.png b/src/algorithms/sorting/bucket-sort/images/bucket_sort_2.png
new file mode 100644
index 0000000000..7b6f32c752
Binary files /dev/null and b/src/algorithms/sorting/bucket-sort/images/bucket_sort_2.png differ
diff --git a/src/algorithms/sorting/radix-sort/README.md b/src/algorithms/sorting/radix-sort/README.md
index 80129278d1..108d7d19b6 100644
--- a/src/algorithms/sorting/radix-sort/README.md
+++ b/src/algorithms/sorting/radix-sort/README.md
@@ -3,37 +3,37 @@
 _Read this in other languages:_
 [_Português_](README.pt-BR.md),
 
-In computer science, **radix sort** is a non-comparative integer sorting 
-algorithm that sorts data with integer keys by grouping keys by the individual 
+In computer science, **radix sort** is a non-comparative integer sorting
+algorithm that sorts data with integer keys by grouping keys by the individual
 digits which share the same significant position and value. A positional notation
-is required, but because integers can represent strings of characters 
-(e.g., names or dates) and specially formatted floating point numbers, radix 
+is required, but because integers can represent strings of characters
+(e.g., names or dates) and specially formatted floating point numbers, radix
 sort is not limited to integers.
 
 *Where does the name come from?*
 
 In mathematical numeral systems, the *radix* or base is the number of unique digits,
-including the digit zero, used to represent numbers in a positional numeral system. 
-For example, a binary system (using numbers 0 and 1) has a radix of 2 and a decimal 
+including the digit zero, used to represent numbers in a positional numeral system.
+For example, a binary system (using numbers 0 and 1) has a radix of 2 and a decimal
 system (using numbers 0 to 9) has a radix of 10.
 
 ## Efficiency
 
-The topic of the efficiency of radix sort compared to other sorting algorithms is 
-somewhat tricky and subject to quite a lot of misunderstandings. Whether radix 
-sort is equally efficient, less efficient or more efficient than the best 
-comparison-based algorithms depends on the details of the assumptions made. 
-Radix sort complexity is `O(wn)` for `n` keys which are integers of word size `w`. 
-Sometimes `w` is presented as a constant, which would make radix sort better 
-(for sufficiently large `n`) than the best comparison-based sorting algorithms, 
-which all perform `O(n log n)` comparisons to sort `n` keys. However, in 
-general `w` cannot be considered a constant: if all `n` keys are distinct, 
-then `w` has to be at least `log n` for a random-access machine to be able to 
-store them in memory, which gives at best a time complexity `O(n log n)`. That 
-would seem to make radix sort at most equally efficient as the best 
+The topic of the efficiency of radix sort compared to other sorting algorithms is
+somewhat tricky and subject to quite a lot of misunderstandings. Whether radix
+sort is equally efficient, less efficient or more efficient than the best
+comparison-based algorithms depends on the details of the assumptions made.
+Radix sort complexity is `O(wn)` for `n` keys which are integers of word size `w`.
+Sometimes `w` is presented as a constant, which would make radix sort better
+(for sufficiently large `n`) than the best comparison-based sorting algorithms,
+which all perform `O(n log n)` comparisons to sort `n` keys. However, in
+general `w` cannot be considered a constant: if all `n` keys are distinct,
+then `w` has to be at least `log n` for a random-access machine to be able to
+store them in memory, which gives at best a time complexity `O(n log n)`. That
+would seem to make radix sort at most equally efficient as the best
 comparison-based sorts (and worse if keys are much longer than `log n`).
 
-![Radix Sort](https://www.researchgate.net/publication/291086231/figure/fig1/AS:614214452404240@1523451545568/Simplistic-illustration-of-the-steps-performed-in-a-radix-sort-In-this-example-the.png)
+![Radix Sort](./images/radix-sort.png)
 
 ## Complexity
 
diff --git a/src/algorithms/sorting/radix-sort/images/radix-sort.png b/src/algorithms/sorting/radix-sort/images/radix-sort.png
new file mode 100644
index 0000000000..0cf9bf9e4d
Binary files /dev/null and b/src/algorithms/sorting/radix-sort/images/radix-sort.png differ
diff --git a/src/algorithms/stack/valid-parentheses/README.md b/src/algorithms/stack/valid-parentheses/README.md
new file mode 100644
index 0000000000..31d7fd60f3
--- /dev/null
+++ b/src/algorithms/stack/valid-parentheses/README.md
@@ -0,0 +1,44 @@
+# Valid Parentheses Problem
+
+Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.
+
+An input string is valid if:
+
+Open brackets must be closed by the same type of brackets.
+Open brackets must be closed in the correct order.
+Every close bracket has a corresponding open bracket of the same type.
+ 
+
+Example 1:
+
+`Input: s = "()"`
+
+Output: true
+
+Example 2:
+
+`Input: s = "()[]{}"`
+
+Output: true
+
+Example 3:
+
+`Input: s = "(]"`
+
+Output: false
+
+This is actually a very common interview question and a very good example of how to use a stack data structure to solve problems. 
+
+## Solution
+The problem can be solved in two ways
+
+### Bruteforce Approach
+We can iterate through the string and then for each character in the string, we check for it's last closing character in the the string. Once we find the last closing character in the string, we remove both characters and then repeat the iteration, if we don't find a closing character for an opening character, then the string is invalid. The time complexity of this would be O(n^2) which is not so efficient.
+
+### Using a Stack
+We can use a hashtable to store all opening characters and the value would be the respective closing character. We can then iterate through the string and if we encounter an opening parantheses, we push it's closing character to the stack. If we ecounter a closing paraentheses, then we pop the stack and confirm that the popped element is equal to the current closing parentheses character. If it is not then the string is invalid. At the end of the iteration, we also need to check that the stack is empty. If it is not then the string is invalid. If it is, then the string is valid. This is a more efficient approach with a Time complexity and Space complexity of O(n).
+
+
+## References
+
+- [Leetcode](https://leetcode.com/problems/valid-parentheses/)
diff --git a/src/algorithms/stack/valid-parentheses/__test__/validParentheses.test.js b/src/algorithms/stack/valid-parentheses/__test__/validParentheses.test.js
new file mode 100644
index 0000000000..e286688dfd
--- /dev/null
+++ b/src/algorithms/stack/valid-parentheses/__test__/validParentheses.test.js
@@ -0,0 +1,23 @@
+import isValid from '../validParentheses';
+
+describe('validParentheses', () => {
+  it('should return false when string is empty', () => {
+    expect(isValid('')).toBe(false);
+  });
+
+  it('should return true when string contains valid parentheses in correct order', () => {
+    expect(isValid('()')).toBe(true);
+    expect(isValid('()[]{}')).toBe(true);
+    expect(isValid('((({[]})))')).toBe(true);
+  });
+
+  it('should return false when string contains invalid parentheses', () => {
+    expect(isValid('(]')).toBe(false);
+    expect(isValid('()[]{} }')).toBe(false);
+    expect(isValid('((({[(]})))')).toBe(false);
+  });
+
+  it('should return false when string contains valid parentheses in wrong order', () => {
+    expect(isValid('({)}')).toBe(false);
+  });
+});
diff --git a/src/algorithms/stack/valid-parentheses/validParentheses.js b/src/algorithms/stack/valid-parentheses/validParentheses.js
new file mode 100644
index 0000000000..a327780768
--- /dev/null
+++ b/src/algorithms/stack/valid-parentheses/validParentheses.js
@@ -0,0 +1,42 @@
+import Stack from '../../../data-structures/stack/Stack';
+import HashTable from '../../../data-structures/hash-table/HashTable';
+
+// Declare hashtable containg opening parentheses as key and it's closing parentheses as value.
+const hashTable = new HashTable(3);
+hashTable.set('{', '}');
+hashTable.set('(', ')');
+hashTable.set('[', ']');
+
+/**
+ * Check if string has valid parentheses.
+ *
+ * @param {string} parenthesesString
+ * @return {boolean}
+ */
+export default function isValid(parenthesesString) {
+  // If string is empty return false
+  if (parenthesesString.length === 0) {
+    return false;
+  }
+  // Create stack
+  const stack = new Stack();
+
+  // Loop through each character of string
+  for (let i = 0; i < parenthesesString.length; i += 1) {
+    const currentCharacter = parenthesesString[i];
+    // If character is opening parentheses push it's closing parentheses to stack
+    if (hashTable.has(currentCharacter)) {
+      stack.push(hashTable.get(currentCharacter));
+    } else {
+      /* If character is a closing parentheses then,:
+      check If stack is empty, if it is return false.
+      if stack is not empty, pop from stack and compare it with current character.
+      If they are not same return false. */
+      if (stack.isEmpty() || stack.pop() !== currentCharacter) {
+        return false;
+      }
+    }
+  }
+  // If stack is empty return true else return false
+  return stack.isEmpty();
+}
diff --git a/src/algorithms/string/longest-common-substring/__test__/longestCommonSubstring.test.js b/src/algorithms/string/longest-common-substring/__test__/longestCommonSubstring.test.js
index a24c0df55c..8c1cb20703 100644
--- a/src/algorithms/string/longest-common-substring/__test__/longestCommonSubstring.test.js
+++ b/src/algorithms/string/longest-common-substring/__test__/longestCommonSubstring.test.js
@@ -7,6 +7,8 @@ describe('longestCommonSubstring', () => {
     expect(longestCommonSubstring('', 'ABC')).toBe('');
     expect(longestCommonSubstring('ABABC', 'BABCA')).toBe('BABC');
     expect(longestCommonSubstring('BABCA', 'ABCBA')).toBe('ABC');
+    expect(longestCommonSubstring('sea', 'eat')).toBe('ea');
+    expect(longestCommonSubstring('algorithms', 'rithm')).toBe('rithm');
     expect(longestCommonSubstring(
       'Algorithms and data structures implemented in JavaScript',
       'Here you may find Algorithms and data structures that are implemented in JavaScript',
diff --git a/src/algorithms/string/longest-common-substring/longestCommonSubstring.js b/src/algorithms/string/longest-common-substring/longestCommonSubstring.js
index 1d68ede9e3..6afeccf398 100644
--- a/src/algorithms/string/longest-common-substring/longestCommonSubstring.js
+++ b/src/algorithms/string/longest-common-substring/longestCommonSubstring.js
@@ -1,4 +1,6 @@
 /**
+ * Longest Common Substring (LCS) (Dynamic Programming Approach).
+ *
  * @param {string} string1
  * @param {string} string2
  * @return {string}
diff --git a/src/algorithms/uncategorized/best-time-to-buy-sell-stocks/README.md b/src/algorithms/uncategorized/best-time-to-buy-sell-stocks/README.md
index 9446993e98..975eade3a4 100644
--- a/src/algorithms/uncategorized/best-time-to-buy-sell-stocks/README.md
+++ b/src/algorithms/uncategorized/best-time-to-buy-sell-stocks/README.md
@@ -46,7 +46,7 @@ Let's say we have an array of prices `[7, 6, 4, 3, 1]` and we're on the _1st_ da
 1. _Option 1: Keep the money_ → profit would equal to the profit from buying/selling the rest of the stocks → `keepProfit = profit([6, 4, 3, 1])`.
 2. _Option 2: Buy/sell at current price_ → profit in this case would equal to the profit from buying/selling the rest of the stocks plus (or minus, depending on whether we're selling or buying) the current stock price → `buySellProfit = -7 + profit([6, 4, 3, 1])`.
 
-The overall profit would be equal to → `overalProfit = Max(keepProfit, buySellProfit)`.
+The overall profit would be equal to → `overallProfit = Max(keepProfit, buySellProfit)`.
 
 As you can see the `profit([6, 4, 3, 1])` task is being solved in the same recursive manner.
 
diff --git a/src/algorithms/uncategorized/n-queens/README.md b/src/algorithms/uncategorized/n-queens/README.md
index 077e26d73d..399256768e 100644
--- a/src/algorithms/uncategorized/n-queens/README.md
+++ b/src/algorithms/uncategorized/n-queens/README.md
@@ -59,7 +59,7 @@ and return false.
         queen here leads to a solution.
     b) If placing queen in [row, column] leads to a solution then return 
         true.
-    c) If placing queen doesn't lead to a solution then umark this [row, 
+    c) If placing queen doesn't lead to a solution then unmark this [row, 
         column] (Backtrack) and go to step (a) to try other rows.
 3) If all rows have been tried and nothing worked, return false to trigger 
     backtracking.
diff --git a/src/data-structures/bloom-filter/README.md b/src/data-structures/bloom-filter/README.md
index e156310cb4..7880a7f7be 100644
--- a/src/data-structures/bloom-filter/README.md
+++ b/src/data-structures/bloom-filter/README.md
@@ -93,7 +93,7 @@ three factors: the size of the bloom filter, the
 number of hash functions we use, and the number
 of items that have been inserted into the filter.
 
-The formula to calculate probablity of a false positive is:
+The formula to calculate probability of a false positive is:
 
 ( 1 - e <sup>-kn/m</sup> ) <sup>k</sup>
 
diff --git a/src/data-structures/disjoint-set/DisjointSetAdhoc.js b/src/data-structures/disjoint-set/DisjointSetAdhoc.js
new file mode 100644
index 0000000000..9653418042
--- /dev/null
+++ b/src/data-structures/disjoint-set/DisjointSetAdhoc.js
@@ -0,0 +1,78 @@
+/**
+ * The minimalistic (ad hoc) version of a DisjointSet (or a UnionFind) data structure
+ * that doesn't have external dependencies and that is easy to copy-paste and
+ * use during the coding interview if allowed by the interviewer (since many
+ * data structures in JS are missing).
+ *
+ * Time Complexity:
+ *
+ * - Constructor: O(N)
+ * - Find: O(α(N))
+ * - Union: O(α(N))
+ * - Connected: O(α(N))
+ *
+ * Where N is the number of vertices in the graph.
+ * α refers to the Inverse Ackermann function.
+ * In practice, we assume it's a constant.
+ * In other words, O(α(N)) is regarded as O(1) on average.
+ */
+class DisjointSetAdhoc {
+  /**
+   * Initializes the set of specified size.
+   * @param {number} size
+   */
+  constructor(size) {
+    // The index of a cell is an id of the node in a set.
+    // The value of a cell is an id (index) of the root node.
+    // By default, the node is a parent of itself.
+    this.roots = new Array(size).fill(0).map((_, i) => i);
+
+    // Using the heights array to record the height of each node.
+    // By default each node has a height of 1 because it has no children.
+    this.heights = new Array(size).fill(1);
+  }
+
+  /**
+   * Finds the root of node `a`
+   * @param {number} a
+   * @returns {number}
+   */
+  find(a) {
+    if (a === this.roots[a]) return a;
+    this.roots[a] = this.find(this.roots[a]);
+    return this.roots[a];
+  }
+
+  /**
+   * Joins the `a` and `b` nodes into same set.
+   * @param {number} a
+   * @param {number} b
+   * @returns {number}
+   */
+  union(a, b) {
+    const aRoot = this.find(a);
+    const bRoot = this.find(b);
+
+    if (aRoot === bRoot) return;
+
+    if (this.heights[aRoot] > this.heights[bRoot]) {
+      this.roots[bRoot] = aRoot;
+    } else if (this.heights[aRoot] < this.heights[bRoot]) {
+      this.roots[aRoot] = bRoot;
+    } else {
+      this.roots[bRoot] = aRoot;
+      this.heights[aRoot] += 1;
+    }
+  }
+
+  /**
+   * Checks if `a` and `b` belong to the same set.
+   * @param {number} a
+   * @param {number} b
+   */
+  connected(a, b) {
+    return this.find(a) === this.find(b);
+  }
+}
+
+export default DisjointSetAdhoc;
diff --git a/src/data-structures/disjoint-set/README.md b/src/data-structures/disjoint-set/README.md
index 44bcfa2769..772f68191b 100644
--- a/src/data-structures/disjoint-set/README.md
+++ b/src/data-structures/disjoint-set/README.md
@@ -19,6 +19,11 @@ _MakeSet_ creates 8 singletons.
 
 After some operations of _Union_, some sets are grouped together.
 
+## Implementation
+
+- [DisjointSet.js](./DisjointSet.js)
+- [DisjointSetAdhoc.js](./DisjointSetAdhoc.js) - The minimalistic (ad hoc) version of a DisjointSet (or a UnionFind) data structure that doesn't have external dependencies and that is easy to copy-paste and use during the coding interview if allowed by the interviewer (since many data structures in JS are missing).
+
 ## References
 
 - [Wikipedia](https://en.wikipedia.org/wiki/Disjoint-set_data_structure)
diff --git a/src/data-structures/disjoint-set/__test__/DisjointSetAdhoc.test.js b/src/data-structures/disjoint-set/__test__/DisjointSetAdhoc.test.js
new file mode 100644
index 0000000000..f7be0fcde9
--- /dev/null
+++ b/src/data-structures/disjoint-set/__test__/DisjointSetAdhoc.test.js
@@ -0,0 +1,50 @@
+import DisjointSetAdhoc from '../DisjointSetAdhoc';
+
+describe('DisjointSetAdhoc', () => {
+  it('should create unions and find connected elements', () => {
+    const set = new DisjointSetAdhoc(10);
+
+    // 1-2-5-6-7 3-8-9 4
+    set.union(1, 2);
+    set.union(2, 5);
+    set.union(5, 6);
+    set.union(6, 7);
+
+    set.union(3, 8);
+    set.union(8, 9);
+
+    expect(set.connected(1, 5)).toBe(true);
+    expect(set.connected(5, 7)).toBe(true);
+    expect(set.connected(3, 8)).toBe(true);
+
+    expect(set.connected(4, 9)).toBe(false);
+    expect(set.connected(4, 7)).toBe(false);
+
+    // 1-2-5-6-7 3-8-9-4
+    set.union(9, 4);
+
+    expect(set.connected(4, 9)).toBe(true);
+    expect(set.connected(4, 3)).toBe(true);
+    expect(set.connected(8, 4)).toBe(true);
+
+    expect(set.connected(8, 7)).toBe(false);
+    expect(set.connected(2, 3)).toBe(false);
+  });
+
+  it('should keep the height of the tree small', () => {
+    const set = new DisjointSetAdhoc(10);
+
+    // 1-2-6-7-9 1 3 4 5
+    set.union(7, 6);
+    set.union(1, 2);
+    set.union(2, 6);
+    set.union(1, 7);
+    set.union(9, 1);
+
+    expect(set.connected(1, 7)).toBe(true);
+    expect(set.connected(6, 9)).toBe(true);
+    expect(set.connected(4, 9)).toBe(false);
+
+    expect(Math.max(...set.heights)).toBe(3);
+  });
+});
diff --git a/src/data-structures/heap/Heap.js b/src/data-structures/heap/Heap.js
index 45dfcfa267..b978739ec8 100644
--- a/src/data-structures/heap/Heap.js
+++ b/src/data-structures/heap/Heap.js
@@ -279,7 +279,7 @@ export default class Heap {
   /* istanbul ignore next */
   pairIsInCorrectOrder(firstElement, secondElement) {
     throw new Error(`
-      You have to implement heap pair comparision method
+      You have to implement heap pair comparison method
       for ${firstElement} and ${secondElement} values.
     `);
   }
diff --git a/src/data-structures/heap/MaxHeapAdhoc.js b/src/data-structures/heap/MaxHeapAdhoc.js
new file mode 100644
index 0000000000..b9d69c59e0
--- /dev/null
+++ b/src/data-structures/heap/MaxHeapAdhoc.js
@@ -0,0 +1,115 @@
+/**
+ * The minimalistic (ad hoc) version of a MaxHeap data structure that doesn't have
+ * external dependencies and that is easy to copy-paste and use during the
+ * coding interview if allowed by the interviewer (since many data
+ * structures in JS are missing).
+ */
+class MaxHeapAdhoc {
+  constructor(heap = []) {
+    this.heap = [];
+    heap.forEach(this.add);
+  }
+
+  add(num) {
+    this.heap.push(num);
+    this.heapifyUp();
+  }
+
+  peek() {
+    return this.heap[0];
+  }
+
+  poll() {
+    if (this.heap.length === 0) return undefined;
+    const top = this.heap[0];
+    this.heap[0] = this.heap[this.heap.length - 1];
+    this.heap.pop();
+    this.heapifyDown();
+    return top;
+  }
+
+  isEmpty() {
+    return this.heap.length === 0;
+  }
+
+  toString() {
+    return this.heap.join(',');
+  }
+
+  heapifyUp() {
+    let nodeIndex = this.heap.length - 1;
+    while (nodeIndex > 0) {
+      const parentIndex = this.getParentIndex(nodeIndex);
+      if (this.heap[parentIndex] >= this.heap[nodeIndex]) break;
+      this.swap(parentIndex, nodeIndex);
+      nodeIndex = parentIndex;
+    }
+  }
+
+  heapifyDown() {
+    let nodeIndex = 0;
+
+    while (
+      (
+        this.hasLeftChild(nodeIndex) && this.heap[nodeIndex] < this.leftChild(nodeIndex)
+      )
+      || (
+        this.hasRightChild(nodeIndex) && this.heap[nodeIndex] < this.rightChild(nodeIndex)
+      )
+    ) {
+      const leftIndex = this.getLeftChildIndex(nodeIndex);
+      const rightIndex = this.getRightChildIndex(nodeIndex);
+      const left = this.leftChild(nodeIndex);
+      const right = this.rightChild(nodeIndex);
+
+      if (this.hasLeftChild(nodeIndex) && this.hasRightChild(nodeIndex)) {
+        if (left >= right) {
+          this.swap(leftIndex, nodeIndex);
+          nodeIndex = leftIndex;
+        } else {
+          this.swap(rightIndex, nodeIndex);
+          nodeIndex = rightIndex;
+        }
+      } else if (this.hasLeftChild(nodeIndex)) {
+        this.swap(leftIndex, nodeIndex);
+        nodeIndex = leftIndex;
+      }
+    }
+  }
+
+  getLeftChildIndex(parentIndex) {
+    return (2 * parentIndex) + 1;
+  }
+
+  getRightChildIndex(parentIndex) {
+    return (2 * parentIndex) + 2;
+  }
+
+  getParentIndex(childIndex) {
+    return Math.floor((childIndex - 1) / 2);
+  }
+
+  hasLeftChild(parentIndex) {
+    return this.getLeftChildIndex(parentIndex) < this.heap.length;
+  }
+
+  hasRightChild(parentIndex) {
+    return this.getRightChildIndex(parentIndex) < this.heap.length;
+  }
+
+  leftChild(parentIndex) {
+    return this.heap[this.getLeftChildIndex(parentIndex)];
+  }
+
+  rightChild(parentIndex) {
+    return this.heap[this.getRightChildIndex(parentIndex)];
+  }
+
+  swap(indexOne, indexTwo) {
+    const tmp = this.heap[indexTwo];
+    this.heap[indexTwo] = this.heap[indexOne];
+    this.heap[indexOne] = tmp;
+  }
+}
+
+export default MaxHeapAdhoc;
diff --git a/src/data-structures/heap/MinHeapAdhoc.js b/src/data-structures/heap/MinHeapAdhoc.js
new file mode 100644
index 0000000000..c70692f3de
--- /dev/null
+++ b/src/data-structures/heap/MinHeapAdhoc.js
@@ -0,0 +1,117 @@
+/**
+ * The minimalistic (ad hoc) version of a MinHeap data structure that doesn't have
+ * external dependencies and that is easy to copy-paste and use during the
+ * coding interview if allowed by the interviewer (since many data
+ * structures in JS are missing).
+ */
+class MinHeapAdhoc {
+  constructor(heap = []) {
+    this.heap = [];
+    heap.forEach(this.add);
+  }
+
+  add(num) {
+    this.heap.push(num);
+    this.heapifyUp();
+  }
+
+  peek() {
+    return this.heap[0];
+  }
+
+  poll() {
+    if (this.heap.length === 0) return undefined;
+    const top = this.heap[0];
+    this.heap[0] = this.heap[this.heap.length - 1];
+    this.heap.pop();
+    this.heapifyDown();
+    return top;
+  }
+
+  isEmpty() {
+    return this.heap.length === 0;
+  }
+
+  toString() {
+    return this.heap.join(',');
+  }
+
+  heapifyUp() {
+    let nodeIndex = this.heap.length - 1;
+    while (nodeIndex > 0) {
+      const parentIndex = this.getParentIndex(nodeIndex);
+      if (this.heap[parentIndex] <= this.heap[nodeIndex]) break;
+      this.swap(parentIndex, nodeIndex);
+      nodeIndex = parentIndex;
+    }
+  }
+
+  heapifyDown() {
+    let nodeIndex = 0;
+
+    while (
+      (
+        this.hasLeftChild(nodeIndex)
+        && this.heap[nodeIndex] > this.leftChild(nodeIndex)
+      )
+      || (
+        this.hasRightChild(nodeIndex)
+        && this.heap[nodeIndex] > this.rightChild(nodeIndex)
+      )
+    ) {
+      const leftIndex = this.getLeftChildIndex(nodeIndex);
+      const rightIndex = this.getRightChildIndex(nodeIndex);
+      const left = this.leftChild(nodeIndex);
+      const right = this.rightChild(nodeIndex);
+
+      if (this.hasLeftChild(nodeIndex) && this.hasRightChild(nodeIndex)) {
+        if (left <= right) {
+          this.swap(leftIndex, nodeIndex);
+          nodeIndex = leftIndex;
+        } else {
+          this.swap(rightIndex, nodeIndex);
+          nodeIndex = rightIndex;
+        }
+      } else if (this.hasLeftChild(nodeIndex)) {
+        this.swap(leftIndex, nodeIndex);
+        nodeIndex = leftIndex;
+      }
+    }
+  }
+
+  getLeftChildIndex(parentIndex) {
+    return 2 * parentIndex + 1;
+  }
+
+  getRightChildIndex(parentIndex) {
+    return 2 * parentIndex + 2;
+  }
+
+  getParentIndex(childIndex) {
+    return Math.floor((childIndex - 1) / 2);
+  }
+
+  hasLeftChild(parentIndex) {
+    return this.getLeftChildIndex(parentIndex) < this.heap.length;
+  }
+
+  hasRightChild(parentIndex) {
+    return this.getRightChildIndex(parentIndex) < this.heap.length;
+  }
+
+  leftChild(parentIndex) {
+    return this.heap[this.getLeftChildIndex(parentIndex)];
+  }
+
+  rightChild(parentIndex) {
+    return this.heap[this.getRightChildIndex(parentIndex)];
+  }
+
+  swap(indexOne, indexTwo) {
+    const tmp = this.heap[indexTwo];
+    this.heap[indexTwo] = this.heap[indexOne];
+    this.heap[indexOne] = tmp;
+  }
+}
+
+export default MinHeapAdhoc;
diff --git a/src/data-structures/heap/README.md b/src/data-structures/heap/README.md
index db5f9e339f..392c5c96bc 100644
--- a/src/data-structures/heap/README.md
+++ b/src/data-structures/heap/README.md
@@ -33,6 +33,36 @@ to the key of `C`
 The node at the "top" of the heap with no parents is
 called the root node.
 
+## Time Complexities
+
+Here are time complexities of various heap data structures. Function names assume a max-heap.
+
+| Operation | find-max | delete-max | insert| increase-key| meld |
+| --------- | -------- | ---------- | ----- | ----------- | ---- |
+| [Binary](https://en.wikipedia.org/wiki/Binary_heap) | `Θ(1)` | `Θ(log n)` | `O(log n)` | `O(log n)` | `Θ(n)` |
+| [Leftist](https://en.wikipedia.org/wiki/Leftist_tree) | `Θ(1)` | `Θ(log n)` | `Θ(log n)` | `O(log n)` | `Θ(log n)` |
+| [Binomial](https://en.wikipedia.org/wiki/Binomial_heap) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `O(log n)` | `O(log n)` |
+| [Fibonacci](https://en.wikipedia.org/wiki/Fibonacci_heap) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `Θ(1)` | `Θ(1)` |
+| [Pairing](https://en.wikipedia.org/wiki/Pairing_heap) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `o(log n)` | `Θ(1)` |
+| [Brodal](https://en.wikipedia.org/wiki/Brodal_queue) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `Θ(1)` | `Θ(1)` |
+| [Rank-pairing](https://en.wikipedia.org/w/index.php?title=Rank-pairing_heap&action=edit&redlink=1) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `Θ(1)` | `Θ(1)` |
+| [Strict Fibonacci](https://en.wikipedia.org/wiki/Fibonacci_heap) | `Θ(1)` | `Θ(log n)` | `Θ(1)` | `Θ(1)` | `Θ(1)` |
+| [2-3 heap](https://en.wikipedia.org/wiki/2%E2%80%933_heap) | `O(log n)` | `O(log n)` | `O(log n)` | `Θ(1)` | `?` |
+
+Where:
+- **find-max (or find-min):** find a maximum item of a max-heap, or a minimum item of a min-heap, respectively (a.k.a. *peek*)
+- **delete-max (or delete-min):** removing the root node of a max heap (or min heap), respectively
+- **insert:** adding a new key to the heap (a.k.a., *push*)
+- **increase-key or decrease-key:** updating a key within a max- or min-heap, respectively
+- **meld:** joining two heaps to form a valid new heap containing all the elements of both, destroying the original heaps.
+
+> In this repository, the [MaxHeap.js](./MaxHeap.js) and [MinHeap.js](./MinHeap.js) are examples of the **Binary** heap.
+
+## Implementation
+
+- [MaxHeap.js](./MaxHeap.js) and [MinHeap.js](./MinHeap.js)
+- [MaxHeapAdhoc.js](./MaxHeapAdhoc.js) and [MinHeapAdhoc.js](./MinHeapAdhoc.js) - The minimalistic (ad hoc) version of a MinHeap/MaxHeap data structure that doesn't have external dependencies and that is easy to copy-paste and use during the coding interview if allowed by the interviewer (since many data structures in JS are missing).
+
 ## References
 
 - [Wikipedia](https://en.wikipedia.org/wiki/Heap_(data_structure))
diff --git a/src/data-structures/heap/__test__/MaxHeapAdhoc.test.js b/src/data-structures/heap/__test__/MaxHeapAdhoc.test.js
new file mode 100644
index 0000000000..0217a98ddf
--- /dev/null
+++ b/src/data-structures/heap/__test__/MaxHeapAdhoc.test.js
@@ -0,0 +1,91 @@
+import MaxHeap from '../MaxHeapAdhoc';
+
+describe('MaxHeapAdhoc', () => {
+  it('should create an empty max heap', () => {
+    const maxHeap = new MaxHeap();
+
+    expect(maxHeap).toBeDefined();
+    expect(maxHeap.peek()).toBe(undefined);
+    expect(maxHeap.isEmpty()).toBe(true);
+  });
+
+  it('should add items to the heap and heapify it up', () => {
+    const maxHeap = new MaxHeap();
+
+    maxHeap.add(5);
+    expect(maxHeap.isEmpty()).toBe(false);
+    expect(maxHeap.peek()).toBe(5);
+    expect(maxHeap.toString()).toBe('5');
+
+    maxHeap.add(3);
+    expect(maxHeap.peek()).toBe(5);
+    expect(maxHeap.toString()).toBe('5,3');
+
+    maxHeap.add(10);
+    expect(maxHeap.peek()).toBe(10);
+    expect(maxHeap.toString()).toBe('10,3,5');
+
+    maxHeap.add(1);
+    expect(maxHeap.peek()).toBe(10);
+    expect(maxHeap.toString()).toBe('10,3,5,1');
+
+    maxHeap.add(1);
+    expect(maxHeap.peek()).toBe(10);
+    expect(maxHeap.toString()).toBe('10,3,5,1,1');
+
+    expect(maxHeap.poll()).toBe(10);
+    expect(maxHeap.toString()).toBe('5,3,1,1');
+
+    expect(maxHeap.poll()).toBe(5);
+    expect(maxHeap.toString()).toBe('3,1,1');
+
+    expect(maxHeap.poll()).toBe(3);
+    expect(maxHeap.toString()).toBe('1,1');
+  });
+
+  it('should poll items from the heap and heapify it down', () => {
+    const maxHeap = new MaxHeap();
+
+    maxHeap.add(5);
+    maxHeap.add(3);
+    maxHeap.add(10);
+    maxHeap.add(11);
+    maxHeap.add(1);
+
+    expect(maxHeap.toString()).toBe('11,10,5,3,1');
+
+    expect(maxHeap.poll()).toBe(11);
+    expect(maxHeap.toString()).toBe('10,3,5,1');
+
+    expect(maxHeap.poll()).toBe(10);
+    expect(maxHeap.toString()).toBe('5,3,1');
+
+    expect(maxHeap.poll()).toBe(5);
+    expect(maxHeap.toString()).toBe('3,1');
+
+    expect(maxHeap.poll()).toBe(3);
+    expect(maxHeap.toString()).toBe('1');
+
+    expect(maxHeap.poll()).toBe(1);
+    expect(maxHeap.toString()).toBe('');
+
+    expect(maxHeap.poll()).toBe(undefined);
+    expect(maxHeap.toString()).toBe('');
+  });
+
+  it('should heapify down through the right branch as well', () => {
+    const maxHeap = new MaxHeap();
+
+    maxHeap.add(3);
+    maxHeap.add(12);
+    maxHeap.add(10);
+
+    expect(maxHeap.toString()).toBe('12,3,10');
+
+    maxHeap.add(11);
+    expect(maxHeap.toString()).toBe('12,11,10,3');
+
+    expect(maxHeap.poll()).toBe(12);
+    expect(maxHeap.toString()).toBe('11,3,10');
+  });
+});
diff --git a/src/data-structures/heap/__test__/MinHeapAdhoc.test.js b/src/data-structures/heap/__test__/MinHeapAdhoc.test.js
new file mode 100644
index 0000000000..766b307ff0
--- /dev/null
+++ b/src/data-structures/heap/__test__/MinHeapAdhoc.test.js
@@ -0,0 +1,91 @@
+import MinHeapAdhoc from '../MinHeapAdhoc';
+
+describe('MinHeapAdhoc', () => {
+  it('should create an empty min heap', () => {
+    const minHeap = new MinHeapAdhoc();
+
+    expect(minHeap).toBeDefined();
+    expect(minHeap.peek()).toBe(undefined);
+    expect(minHeap.isEmpty()).toBe(true);
+  });
+
+  it('should add items to the heap and heapify it up', () => {
+    const minHeap = new MinHeapAdhoc();
+
+    minHeap.add(5);
+    expect(minHeap.isEmpty()).toBe(false);
+    expect(minHeap.peek()).toBe(5);
+    expect(minHeap.toString()).toBe('5');
+
+    minHeap.add(3);
+    expect(minHeap.peek()).toBe(3);
+    expect(minHeap.toString()).toBe('3,5');
+
+    minHeap.add(10);
+    expect(minHeap.peek()).toBe(3);
+    expect(minHeap.toString()).toBe('3,5,10');
+
+    minHeap.add(1);
+    expect(minHeap.peek()).toBe(1);
+    expect(minHeap.toString()).toBe('1,3,10,5');
+
+    minHeap.add(1);
+    expect(minHeap.peek()).toBe(1);
+    expect(minHeap.toString()).toBe('1,1,10,5,3');
+
+    expect(minHeap.poll()).toBe(1);
+    expect(minHeap.toString()).toBe('1,3,10,5');
+
+    expect(minHeap.poll()).toBe(1);
+    expect(minHeap.toString()).toBe('3,5,10');
+
+    expect(minHeap.poll()).toBe(3);
+    expect(minHeap.toString()).toBe('5,10');
+  });
+
+  it('should poll items from the heap and heapify it down', () => {
+    const minHeap = new MinHeapAdhoc();
+
+    minHeap.add(5);
+    minHeap.add(3);
+    minHeap.add(10);
+    minHeap.add(11);
+    minHeap.add(1);
+
+    expect(minHeap.toString()).toBe('1,3,10,11,5');
+
+    expect(minHeap.poll()).toBe(1);
+    expect(minHeap.toString()).toBe('3,5,10,11');
+
+    expect(minHeap.poll()).toBe(3);
+    expect(minHeap.toString()).toBe('5,11,10');
+
+    expect(minHeap.poll()).toBe(5);
+    expect(minHeap.toString()).toBe('10,11');
+
+    expect(minHeap.poll()).toBe(10);
+    expect(minHeap.toString()).toBe('11');
+
+    expect(minHeap.poll()).toBe(11);
+    expect(minHeap.toString()).toBe('');
+
+    expect(minHeap.poll()).toBe(undefined);
+    expect(minHeap.toString()).toBe('');
+  });
+
+  it('should heapify down through the right branch as well', () => {
+    const minHeap = new MinHeapAdhoc();
+
+    minHeap.add(3);
+    minHeap.add(12);
+    minHeap.add(10);
+
+    expect(minHeap.toString()).toBe('3,12,10');
+
+    minHeap.add(11);
+    expect(minHeap.toString()).toBe('3,11,10,12');
+
+    expect(minHeap.poll()).toBe(3);
+    expect(minHeap.toString()).toBe('10,11,12');
+  });
+});
diff --git a/src/data-structures/linked-list/README.es-ES.md b/src/data-structures/linked-list/README.es-ES.md
index c21c48cded..6c6ba7ee87 100644
--- a/src/data-structures/linked-list/README.es-ES.md
+++ b/src/data-structures/linked-list/README.es-ES.md
@@ -7,9 +7,9 @@ _Lee este artículo en otros idiomas:_
 [_Português_](README.pt-BR.md)
 [_English_](README.md)
 
-En ciencias de la computaciòn una **lista enlazada** es una  coleccion linear
-de elementos de datos, en los cuales el orden linear no es dado por
-su posciòn fisica en memoria. En cambio, cada
+En ciencias de la computación una **lista enlazada** es una  colección lineal 
+de elementos, en los cuales el orden lineal no es dado por
+su posición física en memoria. En cambio, cada 
 elemento señala al siguiente. Es una estructura de datos
 que consiste en un grupo de nodos los cuales juntos representan
 una secuencia. En su forma más sencilla, cada nodo está
@@ -19,10 +19,10 @@ permite la inserción o eliminación de elementos
 desde cualquier posición en la secuencia durante la iteración.
 Las variantes más complejas agregan enlaces adicionales, permitiendo
 una eficiente inserción o eliminación desde referencias arbitrarias
-del elemento. Una desventaja de las listas lazadas es que el tiempo de
+del elemento. Una desventaja de las listas enlazadas es que el tiempo de
 acceso es lineal (y difícil de canalizar). Un acceso
 más rápido, como un acceso aleatorio, no es factible. Los arreglos
-tienen una mejor locazion en caché comparados con las listas lazadas.
+tienen una mejor localización en caché comparados con las listas enlazadas.
 
 ![Linked List](./images/linked-list.jpeg)
 
@@ -112,7 +112,7 @@ Remove(head, value)
 end Remove
 ```
 
-### Atrevesar
+### Atravesar
 
 ```text
 Traverse(head)
diff --git a/src/data-structures/linked-list/README.md b/src/data-structures/linked-list/README.md
index bb30b07525..825a0272f4 100644
--- a/src/data-structures/linked-list/README.md
+++ b/src/data-structures/linked-list/README.md
@@ -7,7 +7,7 @@ _Read this in other languages:_
 [_Português_](README.pt-BR.md),
 [_한국어_](README.ko-KR.md),
 [_Español_](README.es-ES.md),
-[_Turkish_](README.tr-TR.md),
+[_Türkçe_](README.tr-TR.md),
 [_Українська_](README.uk-UA.md)
 
 In computer science, a **linked list** is a linear collection
diff --git a/src/data-structures/linked-list/README.uk-UA.md b/src/data-structures/linked-list/README.uk-UA.md
index 5424c58db4..9b34a6d828 100644
--- a/src/data-structures/linked-list/README.uk-UA.md
+++ b/src/data-structures/linked-list/README.uk-UA.md
@@ -1,10 +1,10 @@
 # Зв'язаний список
 
-Зв'язаний список — базова динамічна структура даних в інформатиці, що складається з вузлів, кожен з яких містить як дані, так посилання («зв'язку») на наступний вузол списку. Дана структура дозволяє ефективно додавати та видаляти елементи на довільній позиції у послідовності у процесі ітерації. Більш складні варіанти включають додаткові посилання, що дозволяють ефективно додавати та видаляти довільні елементи.
+Зв'язаний список — базова динамічна структура даних в інформатиці, що складається з вузлів, кожен з яких містить як дані, так і посилання («зв'язку») на наступний вузол списку. Ця структура даних дозволяє ефективно додавати та видаляти елементи на довільній позиції у послідовності у процесі ітерації. Більш складні варіанти включають додаткові посилання, що дозволяють ефективно додавати та видаляти довільні елементи.
 
-Принциповою перевагою перед масивом є структурна гнучкість: порядок елементів зв'язкового списку може збігатися з порядком розташування елементів даних у пам'яті комп'ютера, а порядок обходу списку завжди явно задається його внутрішніми зв'язками. Суть переваги у тому, що у багатьох мовах створення масиву вимагає вказати його заздалегідь. Зв'язковий список дозволяє обійти це обмеження.
+Принциповою перевагою перед масивом є структурна гнучкість: порядок елементів зв'язаного списку може збігатися з порядком розташування елементів даних у пам'яті комп'ютера, а порядок обходу списку завжди явно задається його внутрішніми зв'язками. Це важливо, бо у багатьох мовах створення масиву вимагає вказати його розмір заздалегідь. Зв'язаний список дозволяє обійти це обмеження.
 
-Недоліком зв'язкових списків є те, що час доступу є лінійним (і важко для реалізації конвеєрів). Неможливий швидкий доступ (випадковий).
+Недоліком зв'язаних списків є те, що час доступу є лінійним (і важко для реалізації конвеєрів). Неможливий швидкий доступ (випадковий).
 
 ![Linked List](./images/linked-list.jpeg)
 
@@ -17,7 +17,7 @@
 ```text
 Add(value)
   Pre: value - значення, що додається
-  Post: value поміщено в кінець списку
+  Post: value додано в кінець списку
   n ← node(value)
   if head = ø
     head ← n
@@ -32,7 +32,7 @@ end Add
 ```text
 Prepend(value)
  Pre: value - значення, що додається
- Post: value поміщено на початок списку
+ Post: value додано на початку списку
  n ← node(value)
  n.next ← head
  head ← n
@@ -42,7 +42,7 @@ Prepend(value)
 end Prepend
 ```
 
-### Поиск
+### Пошук
 
 ```text
 Contains(head, value)
@@ -60,7 +60,7 @@ Contains(head, value)
 end Contains
 ```
 
-### Вилучення
+### Видалення
 
 ```text
 Remove(head, value)
@@ -94,7 +94,7 @@ Remove(head, value)
 end Remove
 ```
 
-### Обход
+### Обхід
 
 ```text
 Traverse(head)
@@ -108,12 +108,12 @@ Traverse(head)
 end Traverse
 ```
 
-### Зворотний обхід
+### Зворотній обхід
 
 ```text
 ReverseTraversal(head, tail)
-  Pre: head и tail відносяться до одного списку
-  Post: елементи списку пройдено у зворотному порядку
+  Pre: head і tail відносяться до одного списку
+  Post: елементи списку пройдено у зворотньому порядку
   if tail != ø
     curr ← tail
     while curr != head
@@ -131,7 +131,7 @@ end ReverseTraversal
 
 ## Складність
 
-### Тимчасова складність
+### Часова складність
 
 | Читання    | Пошук     | Вставка    | Вилучення |
 | :--------: | :-------: | :--------: | :-------: |
@@ -143,5 +143,5 @@ O(n)
 
 ## Посилання
 
-- [Wikipedia](https://uk.wikipedia.org/wiki/%D0%97%D0%B2%27%D1%8F%D0%B7%D0%B0%D0%BD%D0%B8%D0%B9_%D1%81%D0%BF%D0%B8%D1%81%D0%BE%D0%BA)
+- [Wikipedia](https://uk.wikipedia.org/wiki/Зв'язаний_список)
 - [YouTube](https://www.youtube.com/watch?v=6snsMa4E1Os)
diff --git a/src/data-structures/linked-list/README.vi-VN.md b/src/data-structures/linked-list/README.vi-VN.md
new file mode 100644
index 0000000000..005838c6c1
--- /dev/null
+++ b/src/data-structures/linked-list/README.vi-VN.md
@@ -0,0 +1,155 @@
+# Danh sách liên kết (Linked List)
+
+_Đọc bằng ngôn ngữ khác:_
+[_简体中文_](README.zh-CN.md),
+[_Русский_](README.ru-RU.md),
+[_日本語_](README.ja-JP.md),
+[_Português_](README.pt-BR.md),
+[_한국어_](README.ko-KR.md),
+[_Español_](README.es-ES.md),
+[_Türkçe_](README.tr-TR.md),
+[_Українська_](README.uk-UA.md)
+
+
+Trong khoa học máy tính, một danh sách liên kết là một bộ sưu tập tuyến tính
+các phần tử dữ liệu, trong đó thứ tự tuyến tính không được xác định bởi
+vị trí vật lý của chúng trong bộ nhớ. Thay vào đó, mỗi
+phần tử trỏ đến phần tử tiếp theo. Đây là một cấu trúc dữ liệu
+bao gồm một nhóm các nút cùng đại diện cho
+một chuỗi. Dưới dạng đơn giản nhất, mỗi nút
+bao gồm dữ liệu và một tham chiếu (nói cách khác,
+một liên kết) đến nút tiếp theo trong chuỗi. Cấu trúc này
+cho phép việc chèn hoặc loại bỏ các phần tử một cách hiệu quả
+từ bất kỳ vị trí nào trong chuỗi trong quá trình lặp.
+Các biến thể phức tạp hơn thêm các liên kết bổ sung, cho phép
+việc chèn hoặc loại bỏ một cách hiệu quả từ bất kỳ phần tử nào
+trong chuỗi dựa trên tham chiếu. Một nhược điểm của danh sách liên kết
+là thời gian truy cập tuyến tính (và khó điều chỉnh). Truy cập nhanh hơn,
+như truy cập ngẫu nhiên, là không khả thi. Mảng
+có độ tương phản cache tốt hơn so với danh sách liên kết.
+
+![Linked List](./images/linked-list.jpeg)
+*Được làm từ [okso.app](https://okso.app)*
+
+## Mã giải (Pseudocode) cho Các Hoạt Động Cơ Bản
+  *head = đầu,
+  *tail = đuôi,
+  *next = kế tiếp,
+  *node = nút,
+  *value = giá trị
+
+### Chèn (Insert)
+
+```
+ThêmGiáTrị(giá trị) (Add(value))
+  Trước(Pre): giá trị là giá trị muốn thêm vào danh sách
+  Sau(Post): giá trị đã được đặt ở cuối danh sách
+
+ n ← node(value)
+  if head = ø
+    head ← n
+    tail ← n
+  else
+    tail.next ← n
+    tail ← n
+  end if
+end ThêmGiáTrị(Add)
+```
+
+```
+ChènVàoĐầu(giá trị)
+  Trước(Pre): giá trị là giá trị muốn thêm vào danh sách
+  Sau(Post): giá trị đã được đặt ở đầu danh sách
+
+ n ← node(value)
+ n.next ← head
+ head ← n
+ if tail = ø
+   tail ← n
+ end
+end ChènVàoĐầu
+```
+
+### Tìm Kiếm (Search)
+```
+Chứa(đầu, giá trị)
+  Trước: đầu là nút đầu trong danh sách
+       giá trị là giá trị cần tìm kiếm
+  Sau: mục đó có thể ở trong danh sách liên kết, true; nếu không, là false
+  n ← head
+  while n != ø and n.value != value
+    n ← n.next
+  end while
+  if n = ø
+    return false
+  end if
+  return true
+end Contains
+```
+
+### Xóa (Delete)
+```
+Xóa(đầu, giá trị)
+  Trước: đầu là nút đầu trong danh sách
+       giá trị là giá trị cần xóa khỏi danh sách
+  Sau: giá trị đã được xóa khỏi danh sách, true; nếu không, là false
+  if head = ø
+    return false
+  end if
+  n ← head
+  if n.value = value
+    if head = tail
+      head ← ø
+      tail ← ø
+    else
+      head ← head.next
+    end if
+    return true
+  end if
+  while n.next != ø and n.next.value != value
+    n ← n.next
+  end while
+  if n.next != ø
+    if n.next = tail
+      tail ← n
+      tail.next = null
+    else
+      n.next ← n.next.next
+    end if
+    return true
+  end if
+  return false
+end Remove
+```
+
+### Duyệt(raverse)
+Duyệt(đầu)
+  Trước: đầu là nút đầu trong danh sách
+  Sau: các mục trong danh sách đã được duyệt
+  n ← head
+  while n != ø
+    yield n.value
+    n ← n.next
+  end while
+end Traverse
+
+### Duyệt Ngược (Traverse in Reverse)
+DuyệtNgược(đầu, đuôi)
+  Trước: đầu và đuôi thuộc cùng một danh sách
+  Sau: các mục trong danh sách đã được duyệt theo thứ tự ngược lại
+
+## Độ Phức Tạp
+
+### Độ Phức Tạp Thời Gian (Time Complexity)
+
+| Access    | Search    | Insertion | Deletion  |
+| :-------: | :-------: | :-------: | :-------: |
+| O(n)      | O(n)      | O(1)      | O(n)      |
+
+## Độ Phức Tạp Không Gian (Space Complexity)
+O(n)
+
+## Tham Khảo
+
+- [Wikipedia](https://en.wikipedia.org/wiki/Linked_list)
+- [YouTube](https://www.youtube.com/watch?v=njTh_OwMljA&index=2&t=1s&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
diff --git a/src/data-structures/lru-cache/LRUCache.js b/src/data-structures/lru-cache/LRUCache.js
new file mode 100644
index 0000000000..d6b4b3c6be
--- /dev/null
+++ b/src/data-structures/lru-cache/LRUCache.js
@@ -0,0 +1,153 @@
+/* eslint-disable no-param-reassign, max-classes-per-file */
+
+/**
+ * Simple implementation of the Doubly-Linked List Node
+ * that is used in LRUCache class below.
+ */
+class LinkedListNode {
+  /**
+   * Creates a doubly-linked list node.
+   * @param {string} key
+   * @param {any} val
+   * @param {LinkedListNode} prev
+   * @param {LinkedListNode} next
+   */
+  constructor(key, val, prev = null, next = null) {
+    this.key = key;
+    this.val = val;
+    this.prev = prev;
+    this.next = next;
+  }
+}
+
+/**
+ * Implementation of the LRU (Least Recently Used) Cache
+ * based on the HashMap and Doubly Linked List data-structures.
+ *
+ * Current implementation allows to have fast O(1) (in average) read and write operations.
+ *
+ * At any moment in time the LRU Cache holds not more that "capacity" number of items in it.
+ */
+class LRUCache {
+  /**
+   * Creates a cache instance of a specific capacity.
+   * @param {number} capacity
+   */
+  constructor(capacity) {
+    this.capacity = capacity; // How many items to store in cache at max.
+    this.nodesMap = {}; // The quick links to each linked list node in cache.
+    this.size = 0; // The number of items that is currently stored in the cache.
+    this.head = new LinkedListNode(); // The Head (first) linked list node.
+    this.tail = new LinkedListNode(); // The Tail (last) linked list node.
+  }
+
+  /**
+   * Returns the cached value by its key.
+   * Time complexity: O(1) in average.
+   * @param {string} key
+   * @returns {any}
+   */
+  get(key) {
+    if (this.nodesMap[key] === undefined) return undefined;
+    const node = this.nodesMap[key];
+    this.promote(node);
+    return node.val;
+  }
+
+  /**
+   * Sets the value to cache by its key.
+   * Time complexity: O(1) in average.
+   * @param {string} key
+   * @param {any} val
+   */
+  set(key, val) {
+    if (this.nodesMap[key]) {
+      const node = this.nodesMap[key];
+      node.val = val;
+      this.promote(node);
+    } else {
+      const node = new LinkedListNode(key, val);
+      this.append(node);
+    }
+  }
+
+  /**
+   * Promotes the node to the end of the linked list.
+   * It means that the node is most frequently used.
+   * It also reduces the chance for such node to get evicted from cache.
+   * @param {LinkedListNode} node
+   */
+  promote(node) {
+    this.evict(node);
+    this.append(node);
+  }
+
+  /**
+   * Appends a new node to the end of the cache linked list.
+   * @param {LinkedListNode} node
+   */
+  append(node) {
+    this.nodesMap[node.key] = node;
+
+    if (!this.head.next) {
+      // First node to append.
+      this.head.next = node;
+      this.tail.prev = node;
+      node.prev = this.head;
+      node.next = this.tail;
+    } else {
+      // Append to an existing tail.
+      const oldTail = this.tail.prev;
+      oldTail.next = node;
+      node.prev = oldTail;
+      node.next = this.tail;
+      this.tail.prev = node;
+    }
+
+    this.size += 1;
+
+    if (this.size > this.capacity) {
+      this.evict(this.head.next);
+    }
+  }
+
+  /**
+   * Evicts (removes) the node from cache linked list.
+   * @param {LinkedListNode} node
+   */
+  evict(node) {
+    delete this.nodesMap[node.key];
+    this.size -= 1;
+
+    const prevNode = node.prev;
+    const nextNode = node.next;
+
+    // If one and only node.
+    if (prevNode === this.head && nextNode === this.tail) {
+      this.head.next = null;
+      this.tail.prev = null;
+      this.size = 0;
+      return;
+    }
+
+    // If this is a Head node.
+    if (prevNode === this.head) {
+      nextNode.prev = this.head;
+      this.head.next = nextNode;
+      return;
+    }
+
+    // If this is a Tail node.
+    if (nextNode === this.tail) {
+      prevNode.next = this.tail;
+      this.tail.prev = prevNode;
+      return;
+    }
+
+    // If the node is in the middle.
+    prevNode.next = nextNode;
+    nextNode.prev = prevNode;
+  }
+}
+
+export default LRUCache;
diff --git a/src/data-structures/lru-cache/LRUCacheOnMap.js b/src/data-structures/lru-cache/LRUCacheOnMap.js
new file mode 100644
index 0000000000..8e4e226129
--- /dev/null
+++ b/src/data-structures/lru-cache/LRUCacheOnMap.js
@@ -0,0 +1,53 @@
+/* eslint-disable no-restricted-syntax, no-unreachable-loop */
+
+/**
+ * Implementation of the LRU (Least Recently Used) Cache
+ * based on the (ordered) Map data-structure.
+ *
+ * Current implementation allows to have fast O(1) (in average) read and write operations.
+ *
+ * At any moment in time the LRU Cache holds not more that "capacity" number of items in it.
+ */
+class LRUCacheOnMap {
+  /**
+   * Creates a cache instance of a specific capacity.
+   * @param {number} capacity
+   */
+  constructor(capacity) {
+    this.capacity = capacity; // How many items to store in cache at max.
+    this.items = new Map(); // The ordered hash map of all cached items.
+  }
+
+  /**
+   * Returns the cached value by its key.
+   * Time complexity: O(1) in average.
+   * @param {string} key
+   * @returns {any}
+   */
+  get(key) {
+    if (!this.items.has(key)) return undefined;
+    const val = this.items.get(key);
+    this.items.delete(key);
+    this.items.set(key, val);
+    return val;
+  }
+
+  /**
+   * Sets the value to cache by its key.
+   * Time complexity: O(1).
+   * @param {string} key
+   * @param {any} val
+   */
+  set(key, val) {
+    this.items.delete(key);
+    this.items.set(key, val);
+    if (this.items.size > this.capacity) {
+      for (const headKey of this.items.keys()) {
+        this.items.delete(headKey);
+        break;
+      }
+    }
+  }
+}
+
+export default LRUCacheOnMap;
diff --git a/src/data-structures/lru-cache/README.ko-KR.md b/src/data-structures/lru-cache/README.ko-KR.md
new file mode 100644
index 0000000000..93957bc0ed
--- /dev/null
+++ b/src/data-structures/lru-cache/README.ko-KR.md
@@ -0,0 +1,51 @@
+# LRU 캐시 알고리즘
+
+**LRU 캐시 알고리즘** 은 사용된 순서대로 아이템을 정리함으로써, 오랜 시간 동안 사용되지 않은 아이템을 빠르게 찾아낼 수 있도록 한다.
+
+한방향으로만 옷을 걸 수 있는 옷걸이 행거를 생각해봅시다. 가장 오랫동안 입지 않은 옷을 찾기 위해서는, 행거의 반대쪽 끝을 보면 됩니다.
+
+## 문제 정의
+
+LRUCache 클래스를 구현해봅시다:
+
+- `LRUCache(int capacity)` LRU 캐시를 **양수** 의 `capacity` 로 초기화합니다.
+- `int get(int key)` `key` 가 존재할 경우 `key` 값을 반환하고, 그렇지 않으면 `undefined` 를 반환합니다.
+- `void set(int key, int value)` `key` 가 존재할 경우 `key` 값을 업데이트 하고, 그렇지 않으면 `key-value` 쌍을 캐시에 추가합니다. 만약 이 동작으로 인해 키 개수가 `capacity` 를 넘는 경우, 가장 오래된 키 값을 **제거** 합니다.
+
+`get()` 과 `set()` 함수는 무조건 평균 `O(1)` 의 시간 복잡도 내에 실행되어야 합니다.
+
+## 구현
+
+### 버전 1: 더블 링크드 리스트 + 해시맵
+
+[LRUCache.js](./LRUCache.js) 에서 `LRUCache` 구현체 예시를 확인할 수 있습니다. 예시에서는 (평균적으로) 빠른 `O(1)` 캐시 아이템 접근을 위해 `HashMap` 을 사용했고, (평균적으로) 빠른 `O(1)` 캐시 아이템 수정과 제거를 위해 `DoublyLinkedList` 를 사용했습니다. (허용된 최대의 캐시 용량을 유지하기 위해)
+
+![Linked List](./images/lru-cache.jpg)
+
+_[okso.app](https://okso.app) 으로 만듦_
+
+LRU 캐시가 어떻게 작동하는지 더 많은 예시로 확인하고 싶다면 LRUCache.test.js](./**test**/LRUCache.test.js) 파일을 참고하세요.
+
+### 버전 2: 정렬된 맵
+
+더블 링크드 리스트로 구현한 첫번째 예시는 어떻게 평균 `O(1)` 시간 복잡도가 `set()` 과 `get()` 으로 나올 수 있는지 학습 목적과 이해를 돕기 위해 좋은 예시입니다.
+
+그러나, 더 쉬운 방법은 자바스크립트의 [Map](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map) 객체를 사용하는 것입니다. 이 `Map` 객체는 키-값 쌍과 키를 **추가하는 순서 원본** 을 지닙니다. 우리는 이걸 아이템을 제거하거나 다시 추가하면서 맵의 "가장 마지막" 동작에서 최근에 사용된 아이템을 유지하기 위해 사용할 수 있습니다. `Map` 의 시작점에 있는 아이템은 캐시 용량이 넘칠 경우 가장 먼저 제거되는 대상입니다. 아이템의 순서는 `map.keys()` 와 같은 `IterableIterator` 을 사용해 확인할 수 있습니다.
+
+해당 구현체는 [LRUCacheOnMap.js](./LRUCacheOnMap.js) 의 `LRUCacheOnMap` 예시에서 확인할 수 있습니다.
+
+이 LRU 캐시 방식이 어떻게 작동하는지 더 많은 테스트 케이스를 확인하고 싶다면 [LRUCacheOnMap.test.js](./__test__/LRUCacheOnMap.test.js) 파일을 참고하세요.
+
+## 복잡도
+
+|                 | 평균   |
+| --------------- | ------ |
+| 공간            | `O(n)` |
+| 아이템 찾기     | `O(1)` |
+| 아이템 설정하기 | `O(1)` |
+
+## 참조
+
+- [LRU Cache on LeetCode](https://leetcode.com/problems/lru-cache/solutions/244744/lru-cache/)
+- [LRU Cache on InterviewCake](https://www.interviewcake.com/concept/java/lru-cache)
+- [LRU Cache on Wiki](https://en.wikipedia.org/wiki/Cache_replacement_policies)
diff --git a/src/data-structures/lru-cache/README.md b/src/data-structures/lru-cache/README.md
new file mode 100644
index 0000000000..eaa075f809
--- /dev/null
+++ b/src/data-structures/lru-cache/README.md
@@ -0,0 +1,54 @@
+# Least Recently Used (LRU) Cache
+
+_Read this in other languages:_
+[한국어](README.ko-KR.md),
+
+A **Least Recently Used (LRU) Cache** organizes items in order of use, allowing you to quickly identify which item hasn't been used for the longest amount of time.
+
+Picture a clothes rack, where clothes are always hung up on one side. To find the least-recently used item, look at the item on the other end of the rack.
+
+## The problem statement
+
+Implement the LRUCache class:
+
+- `LRUCache(int capacity)` Initialize the LRU cache with **positive** size `capacity`.
+- `int get(int key)` Return the value of the `key` if the `key` exists, otherwise return `undefined`.
+- `void set(int key, int value)` Update the value of the `key` if the `key` exists. Otherwise, add the `key-value` pair to the cache. If the number of keys exceeds the `capacity` from this operation, **evict** the least recently used key.
+
+The functions `get()` and `set()` must each run in `O(1)` average time complexity.
+
+## Implementation
+
+### Version 1: Doubly Linked List + Hash Map
+
+See the `LRUCache` implementation example in [LRUCache.js](./LRUCache.js). The solution uses a `HashMap` for fast `O(1)` (in average) cache items access, and a `DoublyLinkedList` for fast `O(1)` (in average) cache items promotions and eviction (to keep the maximum allowed cache capacity).
+
+![Linked List](./images/lru-cache.jpg)
+
+_Made with [okso.app](https://okso.app)_
+
+You may also find more test-case examples of how the LRU Cache works in [LRUCache.test.js](./__test__/LRUCache.test.js) file.
+
+### Version 2: Ordered Map
+
+The first implementation that uses doubly linked list is good for learning purposes and for better understanding of how the average `O(1)` time complexity is achievable while doing `set()` and `get()`.
+
+However, the simpler approach might be to use a JavaScript [Map](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Map) object. The `Map` object holds key-value pairs and **remembers the original insertion order** of the keys. We can use this fact in order to keep the recently-used items in the "end" of the map by removing and re-adding items. The item at the beginning of the `Map` is the first one to be evicted if cache capacity overflows. The order of the items may checked by using the `IterableIterator` like `map.keys()`.
+
+See the `LRUCacheOnMap` implementation example in [LRUCacheOnMap.js](./LRUCacheOnMap.js).
+
+You may also find more test-case examples of how the LRU Cache works in [LRUCacheOnMap.test.js](./__test__/LRUCacheOnMap.test.js) file.
+
+## Complexities
+
+|          | Average |
+| -------- | ------- |
+| Space    | `O(n)`  |
+| Get item | `O(1)`  |
+| Set item | `O(1)`  |
+
+## References
+
+- [LRU Cache on LeetCode](https://leetcode.com/problems/lru-cache/solutions/244744/lru-cache/)
+- [LRU Cache on InterviewCake](https://www.interviewcake.com/concept/java/lru-cache)
+- [LRU Cache on Wiki](https://en.wikipedia.org/wiki/Cache_replacement_policies)
diff --git a/src/data-structures/lru-cache/__test__/LRUCache.test.js b/src/data-structures/lru-cache/__test__/LRUCache.test.js
new file mode 100644
index 0000000000..438fd50378
--- /dev/null
+++ b/src/data-structures/lru-cache/__test__/LRUCache.test.js
@@ -0,0 +1,150 @@
+import LRUCache from '../LRUCache';
+
+describe('LRUCache', () => {
+  it('should set and get values to and from the cache', () => {
+    const cache = new LRUCache(100);
+    expect(cache.get('key-1')).toBeUndefined();
+
+    cache.set('key-1', 15);
+    cache.set('key-2', 16);
+    cache.set('key-3', 17);
+    expect(cache.get('key-1')).toBe(15);
+    expect(cache.get('key-2')).toBe(16);
+    expect(cache.get('key-3')).toBe(17);
+    expect(cache.get('key-3')).toBe(17);
+    expect(cache.get('key-2')).toBe(16);
+    expect(cache.get('key-1')).toBe(15);
+
+    cache.set('key-1', 5);
+    cache.set('key-2', 6);
+    cache.set('key-3', 7);
+    expect(cache.get('key-1')).toBe(5);
+    expect(cache.get('key-2')).toBe(6);
+    expect(cache.get('key-3')).toBe(7);
+  });
+
+  it('should evict least recently used items from cache with cache size of 1', () => {
+    const cache = new LRUCache(1);
+    expect(cache.get('key-1')).toBeUndefined();
+
+    cache.set('key-1', 15);
+    expect(cache.get('key-1')).toBe(15);
+
+    cache.set('key-2', 16);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(16);
+
+    cache.set('key-2', 17);
+    expect(cache.get('key-2')).toBe(17);
+
+    cache.set('key-3', 18);
+    cache.set('key-4', 19);
+    expect(cache.get('key-2')).toBeUndefined();
+    expect(cache.get('key-3')).toBeUndefined();
+    expect(cache.get('key-4')).toBe(19);
+  });
+
+  it('should evict least recently used items from cache with cache size of 2', () => {
+    const cache = new LRUCache(2);
+    expect(cache.get('key-21')).toBeUndefined();
+
+    cache.set('key-21', 15);
+    expect(cache.get('key-21')).toBe(15);
+
+    cache.set('key-22', 16);
+    expect(cache.get('key-21')).toBe(15);
+    expect(cache.get('key-22')).toBe(16);
+
+    cache.set('key-22', 17);
+    expect(cache.get('key-22')).toBe(17);
+
+    cache.set('key-23', 18);
+    expect(cache.size).toBe(2);
+    expect(cache.get('key-21')).toBeUndefined();
+    expect(cache.get('key-22')).toBe(17);
+    expect(cache.get('key-23')).toBe(18);
+
+    cache.set('key-24', 19);
+    expect(cache.size).toBe(2);
+    expect(cache.get('key-21')).toBeUndefined();
+    expect(cache.get('key-22')).toBeUndefined();
+    expect(cache.get('key-23')).toBe(18);
+    expect(cache.get('key-24')).toBe(19);
+  });
+
+  it('should evict least recently used items from cache with cache size of 3', () => {
+    const cache = new LRUCache(3);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+
+    cache.set('key-3', 4);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(4);
+
+    cache.set('key-4', 5);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(4);
+    expect(cache.get('key-4')).toBe(5);
+  });
+
+  it('should promote the node while calling set() method', () => {
+    const cache = new LRUCache(2);
+
+    cache.set('2', 1);
+    cache.set('1', 1);
+    cache.set('2', 3);
+    cache.set('4', 1);
+    expect(cache.get('1')).toBeUndefined();
+    expect(cache.get('2')).toBe(3);
+  });
+
+  it('should promote the recently accessed item with cache size of 3', () => {
+    const cache = new LRUCache(3);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    expect(cache.get('key-1')).toBe(1);
+
+    cache.set('key-4', 4);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBe(4);
+    expect(cache.get('key-2')).toBeUndefined();
+  });
+
+  it('should promote the recently accessed item with cache size of 4', () => {
+    const cache = new LRUCache(4);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    cache.set('key-4', 4);
+    expect(cache.get('key-4')).toBe(4);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-1')).toBe(1);
+
+    cache.set('key-5', 5);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBeUndefined();
+    expect(cache.get('key-5')).toBe(5);
+
+    cache.set('key-6', 6);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBeUndefined();
+    expect(cache.get('key-5')).toBe(5);
+    expect(cache.get('key-6')).toBe(6);
+  });
+});
diff --git a/src/data-structures/lru-cache/__test__/LRUCacheOnMap.test.js b/src/data-structures/lru-cache/__test__/LRUCacheOnMap.test.js
new file mode 100644
index 0000000000..8912a1199a
--- /dev/null
+++ b/src/data-structures/lru-cache/__test__/LRUCacheOnMap.test.js
@@ -0,0 +1,148 @@
+import LRUCache from '../LRUCacheOnMap';
+
+describe('LRUCacheOnMap', () => {
+  it('should set and get values to and from the cache', () => {
+    const cache = new LRUCache(100);
+    expect(cache.get('key-1')).toBeUndefined();
+
+    cache.set('key-1', 15);
+    cache.set('key-2', 16);
+    cache.set('key-3', 17);
+    expect(cache.get('key-1')).toBe(15);
+    expect(cache.get('key-2')).toBe(16);
+    expect(cache.get('key-3')).toBe(17);
+    expect(cache.get('key-3')).toBe(17);
+    expect(cache.get('key-2')).toBe(16);
+    expect(cache.get('key-1')).toBe(15);
+
+    cache.set('key-1', 5);
+    cache.set('key-2', 6);
+    cache.set('key-3', 7);
+    expect(cache.get('key-1')).toBe(5);
+    expect(cache.get('key-2')).toBe(6);
+    expect(cache.get('key-3')).toBe(7);
+  });
+
+  it('should evict least recently used items from cache with cache size of 1', () => {
+    const cache = new LRUCache(1);
+    expect(cache.get('key-1')).toBeUndefined();
+
+    cache.set('key-1', 15);
+    expect(cache.get('key-1')).toBe(15);
+
+    cache.set('key-2', 16);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(16);
+
+    cache.set('key-2', 17);
+    expect(cache.get('key-2')).toBe(17);
+
+    cache.set('key-3', 18);
+    cache.set('key-4', 19);
+    expect(cache.get('key-2')).toBeUndefined();
+    expect(cache.get('key-3')).toBeUndefined();
+    expect(cache.get('key-4')).toBe(19);
+  });
+
+  it('should evict least recently used items from cache with cache size of 2', () => {
+    const cache = new LRUCache(2);
+    expect(cache.get('key-21')).toBeUndefined();
+
+    cache.set('key-21', 15);
+    expect(cache.get('key-21')).toBe(15);
+
+    cache.set('key-22', 16);
+    expect(cache.get('key-21')).toBe(15);
+    expect(cache.get('key-22')).toBe(16);
+
+    cache.set('key-22', 17);
+    expect(cache.get('key-22')).toBe(17);
+
+    cache.set('key-23', 18);
+    expect(cache.get('key-21')).toBeUndefined();
+    expect(cache.get('key-22')).toBe(17);
+    expect(cache.get('key-23')).toBe(18);
+
+    cache.set('key-24', 19);
+    expect(cache.get('key-21')).toBeUndefined();
+    expect(cache.get('key-22')).toBeUndefined();
+    expect(cache.get('key-23')).toBe(18);
+    expect(cache.get('key-24')).toBe(19);
+  });
+
+  it('should evict least recently used items from cache with cache size of 3', () => {
+    const cache = new LRUCache(3);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+
+    cache.set('key-3', 4);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(4);
+
+    cache.set('key-4', 5);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(4);
+    expect(cache.get('key-4')).toBe(5);
+  });
+
+  it('should promote the node while calling set() method', () => {
+    const cache = new LRUCache(2);
+
+    cache.set('2', 1);
+    cache.set('1', 1);
+    cache.set('2', 3);
+    cache.set('4', 1);
+    expect(cache.get('1')).toBeUndefined();
+    expect(cache.get('2')).toBe(3);
+  });
+
+  it('should promote the recently accessed item with cache size of 3', () => {
+    const cache = new LRUCache(3);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    expect(cache.get('key-1')).toBe(1);
+
+    cache.set('key-4', 4);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBe(4);
+    expect(cache.get('key-2')).toBeUndefined();
+  });
+
+  it('should promote the recently accessed item with cache size of 4', () => {
+    const cache = new LRUCache(4);
+
+    cache.set('key-1', 1);
+    cache.set('key-2', 2);
+    cache.set('key-3', 3);
+    cache.set('key-4', 4);
+    expect(cache.get('key-4')).toBe(4);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-1')).toBe(1);
+
+    cache.set('key-5', 5);
+    expect(cache.get('key-1')).toBe(1);
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBeUndefined();
+    expect(cache.get('key-5')).toBe(5);
+
+    cache.set('key-6', 6);
+    expect(cache.get('key-1')).toBeUndefined();
+    expect(cache.get('key-2')).toBe(2);
+    expect(cache.get('key-3')).toBe(3);
+    expect(cache.get('key-4')).toBeUndefined();
+    expect(cache.get('key-5')).toBe(5);
+    expect(cache.get('key-6')).toBe(6);
+  });
+});
diff --git a/src/data-structures/lru-cache/images/lru-cache.jpg b/src/data-structures/lru-cache/images/lru-cache.jpg
new file mode 100644
index 0000000000..6a2fcfceb4
Binary files /dev/null and b/src/data-structures/lru-cache/images/lru-cache.jpg differ
diff --git a/src/data-structures/queue/README.vi-VN.md b/src/data-structures/queue/README.vi-VN.md
new file mode 100644
index 0000000000..84b3bdbfa6
--- /dev/null
+++ b/src/data-structures/queue/README.vi-VN.md
@@ -0,0 +1,22 @@
+# Hàng đợi (Queue)
+
+_Đọc bằng ngôn ngữ khác:_
+[_简体中文_](README.zh-CN.md),
+[_Русский_](README.ru-RU.md),
+[_日本語_](README.ja-JP.md),
+[_Português_](README.pt-BR.md),
+[_한국어_](README.ko-KR.md),
+[_Українська_](README.uk-UA.md)
+
+Trong khoa học máy tính, một **hàng đợi** là một loại cụ thể của kiểu dữ liệu trừu tượng hoặc bộ sưu tập trong đó các phần tử trong bộ sưu tập được giữ theo thứ tự và nguyên tắc (hoặc chỉ) các hoạt động trên bộ sưu tập là thêm các phần tử vào vị trí cuối cùng, được gọi là đưa vào hàng đợi (enqueue), và loại bỏ các phần tử từ vị trí đầu tiên, được gọi là đưa ra khỏi hàng đợi (dequeue). Điều này khiến cho hàng đợi trở thành một cấu trúc dữ liệu First-In-First-Out (FIFO). Trong cấu trúc dữ liệu FIFO, phần tử đầu tiên được thêm vào hàng đợi sẽ là phần tử đầu tiên được loại bỏ. Điều này tương đương với yêu cầu rằng sau khi một phần tử mới được thêm vào, tất cả các phần tử đã được thêm vào trước đó phải được loại bỏ trước khi có thể loại bỏ phần tử mới. Thường thì cũng có thêm một hoạt động nhìn hay lấy phần đầu, trả về giá trị của phần tử đầu tiên mà không loại bỏ nó. Hàng đợi là một ví dụ về cấu trúc dữ liệu tuyến tính, hoặc trừu tượng hơn là một bộ sưu tập tuần tự.
+
+Hàng đợi FIFO (First-In-First-Out) có thể được biểu diễn như sau:
+
+![Queue](./images/queue.jpeg)
+
+*Made with [okso.app](https://okso.app)*
+
+## Tham Khảo
+
+- [Wikipedia](https://en.wikipedia.org/wiki/Queue_(abstract_data_type))
+- [YouTube](https://www.youtube.com/watch?v=wjI1WNcIntg&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8&index=3&)
diff --git a/src/data-structures/stack/README.uk-UA.md b/src/data-structures/stack/README.uk-UA.md
index 9941114f5b..f71d2274eb 100644
--- a/src/data-structures/stack/README.uk-UA.md
+++ b/src/data-structures/stack/README.uk-UA.md
@@ -10,8 +10,8 @@
 Додаткова операція для читання головного елемента (peek) дає доступ
 до останнього елементу стека без зміни самого стека.
 
-Найчастіше принцип роботи стека порівнюють зі чаркою тарілок: щоб узяти другу
-зверху потрібно зняти верхню.
+Найчастіше принцип роботи стека порівнюють із стопкою тарілок: щоб узяти другу
+зверху потрібно спочатку зняти верхню.
 
 Ілюстрація роботи зі стеком.
 
diff --git a/src/data-structures/stack/README.vi-VN.md b/src/data-structures/stack/README.vi-VN.md
new file mode 100644
index 0000000000..f051f3201f
--- /dev/null
+++ b/src/data-structures/stack/README.vi-VN.md
@@ -0,0 +1,27 @@
+# Ngăn xếp (stack)
+
+_Đọc bằng ngôn ngữ khác:_
+[_简体中文_](README.zh-CN.md),
+[_Русский_](README.ru-RU.md),
+[_日本語_](README.ja-JP.md),
+[_Português_](README.pt-BR.md),
+[_한국어_](README.ko-KR.md),
+[_Español_](README.es-ES.md),
+[_Українська_](README.uk-UA.md)
+
+Trong khoa học máy tính, một ngăn xếp (stack) là một kiểu dữ liệu trừu tượng phục vụ như một bộ sưu tập các phần tử, với hai hoạt động chính:
+
+đẩy (push), thêm một phần tử vào bộ sưu tập, và
+lấy (pop), loại bỏ phần tử được thêm gần nhất mà chưa được loại bỏ.
+Thứ tự mà các phần tử được lấy ra khỏi ngăn xếp dẫn đến tên gọi thay thế của nó, là LIFO (last in, first out). Ngoài ra, một hoạt động nhìn có thể cung cấp quyền truy cập vào phần trên mà không làm thay đổi ngăn xếp. Tên "ngăn xếp" cho loại cấu trúc này đến từ sự tương tự với một bộ sưu tập các vật phẩm vật lý được xếp chồng lên nhau, điều này làm cho việc lấy một vật phẩm ra khỏi đỉnh của ngăn xếp dễ dàng, trong khi để đến được một vật phẩm sâu hơn trong ngăn xếp có thể đòi hỏi việc lấy ra nhiều vật phẩm khác trước đó.
+
+Biểu diễn đơn giản về thời gian chạy của một ngăn xếp với các hoạt động đẩy và lấy.
+
+![Stack](./images/stack.jpeg)
+
+*Made with [okso.app](https://okso.app)*
+
+## Tham Khảo
+
+- [Wikipedia](https://en.wikipedia.org/wiki/Linked_list)
+- [YouTube](https://www.youtube.com/watch?v=njTh_OwMljA&index=2&t=1s&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
diff --git a/src/data-structures/tree/README.md b/src/data-structures/tree/README.md
index 7eb7ec1db1..e492257d33 100644
--- a/src/data-structures/tree/README.md
+++ b/src/data-structures/tree/README.md
@@ -23,7 +23,7 @@ together with a list of references to nodes (the "children"),
 with the constraints that no reference is duplicated, and none
 points to the root.
 
-A simple unordered tree; in this diagram, the node labeled 7 has
+A simple unordered tree; in this diagram, the node labeled 3 has
 two children, labeled 2 and 6, and one parent, labeled 2. The
 root node, at the top, has no parent.
 
diff --git a/src/data-structures/trie/README.ko-KO.md b/src/data-structures/trie/README.ko-KO.md
new file mode 100644
index 0000000000..e57142ece4
--- /dev/null
+++ b/src/data-structures/trie/README.ko-KO.md
@@ -0,0 +1,19 @@
+# Trie
+
+_Read this in other languages:_
+[_简体中文_](README.zh-CN.md),
+[_Русский_](README.ru-RU.md),
+[_Português_](README.pt-BR.md),
+[_Українська_](README.uk-UA.md),
+[_한국어_](README.ko-KO.md)
+
+컴퓨터 과학에서 **트라이**는 디지털 트리라고도 불리며 때로는 기수 트리 또는 접두사 트리(접두사로 검색할 수 있기 때문에)라고도 불리며 일종의 검색 트리입니다. 키가 보통 문자열인 동적 집합 또는 연관 배열을 저장하는 데 사용되는 순서가 지정된 트리 데이터 구조입니다. 이진 검색 트리와 달리 트리의 어떤 노드도 해당 노드와 연결된 키를 저장하지 않으며 대신 트리의 위치가 해당 노드와 연결된 키를 정의합니다. 노드의 모든 하위 항목은 해당 노드와 연결된 문자열의 공통 접두사를 가지며 루트는 빈 문자열과 연결됩니다. 값은 모든 노드와 반드시 연결되지는 않습니다. 오히려 값은 나뭇잎과 관심 있는 키에 해당하는 일부 내부 노드에만 연결되는 경향이 있습니다. 접두사 트리의 공간에 최적화된 표현은 콤팩트 접두사 트리를 참조하십시오.
+
+![Trie](./images/trie.jpg)
+
+_Made with [okso.app](https://okso.app)_
+
+## 참조
+
+- [Wikipedia](<https://ko.wikipedia.org/wiki/%ED%8A%B8%EB%9D%BC%EC%9D%B4_(%EC%BB%B4%ED%93%A8%ED%8C%85)>)
+- [YouTube](https://www.youtube.com/watch?v=zIjfhVPRZCg&list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8&index=7&t=0s)
diff --git a/src/data-structures/trie/README.md b/src/data-structures/trie/README.md
index 2082fed903..b3bba5de1e 100644
--- a/src/data-structures/trie/README.md
+++ b/src/data-structures/trie/README.md
@@ -4,7 +4,8 @@ _Read this in other languages:_
 [_简体中文_](README.zh-CN.md),
 [_Русский_](README.ru-RU.md),
 [_Português_](README.pt-BR.md),
-[_Українська_](README.uk-UA.md)
+[_Українська_](README.uk-UA.md),
+[_한국어_](README.ko-KO.md)
 
 In computer science, a **trie**, also called digital tree and sometimes
 radix tree or prefix tree (as they can be searched by prefixes),
