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KNOWLEDGE ACQUISITION METHODS
(TSI 2323 – inc. Introduction to Machine Learning) Unit 1 – Introduction
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Group 2 – second years (Cadets)
Comp Science Sem Total Year Sem Unov ALK Short 2 19 21 Wireless Networks Security Digital Forensics 6 Knowledge Based Systems (Fuzzy Logic) Digital Image Processing Elective 2 Elective 3 Elective 4 Project 2 (PSM) 3 Short Plus 3 3 3 3 Ethics Industrial Training 12 12 3 3 3 5 Short ALK 2 4 Project 1 (PSM) 17 21 Information Security Management Ethical Hackers Cryptography Elective 1 4 Knowledge Acquisition Methods (Machine Learning) Computational Intelligence (Intelligent Agents) Data Mining Group Project 6 18 24 3 Software Engineering Data Structures Artificial Intelligence Operating Systems Computer Networks Computer and Network Security ALK Short 1 8 12 20 2 Discrete Mathematics Object Oriented Programming Systems Analysis and Design Web Programming 8 12 20 1 Statistics Database Systems Fundamentals of Programming Computer Organisation and Architecture 28 93 JUMLAH KREDIT 121
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Knowledge Based Systems (Fuzzy Logic) Digital Image Processing
AI Security Project 2 (PSM) Knowledge Based Systems (Fuzzy Logic) Digital Image Processing Industrial Training Project 1 (PSM) Knowledge Acquisition Methods (Machine Learning) Computational Intelligence (Intelligent Agents) Data Mining Group Project Artificial Intelligence Programming Systems Computer and Network Security Software Engineering Web Programming Object Oriented Programming Database Systems Operating Systems Computer Networks Data Structures Systems Analysis and Design Statistics Discrete Mathematics Fundamentals of Programming Computer Organisation and Architecture
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Test Represent Acquire Identify Knowledge Acquisition – Essence
DATA INFORMATION KNOWLEDGE WISDOM IMPLICIT/ TACIT EXPLICIT Understanding relations Understanding patterns Understanding principles Knowledge Engineering Identify Represent Acquire Test Domain Ontology General Ontologies Semantic Networks Cases Data Structured Data (Databases) Data Input (usually manually) Requirements Analysis Data Mining / Machine Learning RAW Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (e.g. numbers, vectors) IMPLICIT/ TACIT
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Knowledge Acquisition – Essence
DATA INFORMATION KNOWLEDGE WISDOM IMPLICIT/ TACIT EXPLICIT Understanding relations Understanding patterns Understanding principles Have an appreciation of how knowledge is acquired. Knowledge Engineering Identify Represent Acquire Test Domain Ontology General Ontologies Semantic Networks Cases Know how to (manually) represent knowledge – Ontology-based This course may appear very complex, but the main points to be learnt are: RAW Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (e.g. numbers, vectors) Attain comfort level with different types of input data.
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Ontology-Based Knowledge Representation
Representation Target Ontologies within the Knowledge Representation Fundamentals (to be given in Chapter 2)
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Within the Curriculum Structure
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Curriculum (Cadets) Industrial Training ALK 120 90 12 3 21 22 20 19 17
15 30 2 5 7 8 Year 1 Sem Short 6 4 Unov Discrete Mathematics Fundamentals of Programming Computer Organisation and Architecture Statistics Data Structures Object Oriented Programming Systems Analysis and Design Operating Systems ALK Software Engineering Web Programming Database Systems Computer Networks Computer and Network Security Artificial Intelligence Computational Intelligence (Intelligent Agents) Data Mining Ethical Hackers Cryptography Elective 1 Knowledge Based Systems (Fuzzy Logic) Digital Image Processing Wireless Networks Security Digital Forensics Elective 3 Elective 2 Elective 4 Ethics Industrial Training Project 1 (PSM) Group Project Project 2 (PSM) Comp Science Sem Total JUMLAH KREDIT Curriculum (Cadets)
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List of Electives (choose 4 of 6)
Data Mining Digital Image Processing (DIP) Knowledge Based System (KBS) Fuzzy Logic, uncertainty, belief nets, Case based reasoning Expert Systems Computational Intelligence (CI) Intelligent Agents, ant colony algorithm, swarm optimization, constraints handling, genetic algorithm, Neural Networks Knowledge Acquisition Methods inc. machine learning, …. Visualisation & Simulation
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Knowledge Acquisition Methods
These will be emphasised on Objectives Unit 1 Introduction to Knowledge Acquisition Knowledge Acquisition (KA) Methods of KA To describe the methods used for knowledge acquisition and knowledge discovery To understand data and knowledge representation in knowledge acquisition To assess the applicability of machine learning algorithms for a specific data mining task To understand various approaches of knowledge acquisition from experts Unit 2 Data Input and Knowledge Representation Types of data input Knowledge representation Reasoning Unit 3 Manual Knowledge Acquisition Requirements analysis Knowledge Engineering Tacit Knowledge Unit 4 Semi-automated Knowledge Acquisition From workflow systems Prototyping Data Mining Unit 5 Automated Knowledge Acquisition Machine Learning: Supervised and unsupervised learning Natural Language Processing Image Processing
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Data Mining and Knowledge Acquisition Methods
Unit 1 Introduction to Data Mining Lectures Unit 1 Introduction to Knowledge Acquisition Lectures Coursework Data mining includes concept of knowledge discovery process, data pre-processing and data mining techniques to find hidden pattern of information from large volume of data using selected data mining tools. Unit 2 Data, Preprocessing and Exploration Unit 2 Data Input and Knowledge Representation Unit 3 Data Mining Tools Unit 3 Manual Knowledge Acquisition Unit 4 Cluster Detection Unit 4 Semi automated Knowledge Acquisition Unit 5 Classification Unit 5 Automated Knowledge Acquisition Unit 6 Association Rules Knowledge acquisition is the process of extracting, structuring and organizing knowledge from human experts and also databases, so it can be used in intelligent systems. Artificial Intelligence Introduction to Artificial Intelligence Heuristic and State Search Knowledge Representations Semantic Query Reasoning Declarative Programming AI Applications
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AI Specialisation - Links
Knowledge Acquisition Methods Data Mining Unit 1 Introduction to Data Mining Unit 1 Introduction to Knowledge Acquisition Data mining tools - WEKA Unit 2 Data, Preprocessing and Exploration Unit 2 Data Input and Knowledge Representation Unit 3 Data Mining Tools Unit 3 Manual Knowledge Acquisition Unit 4 Cluster Detection Unit 4 Semi automated Knowledge Acquisition Knowledge Extraction Unit 5 Classification Knowledge Interpretation Unit 5 Automated Knowledge Acquisition Unit 6 Association Rules
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Coverage
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Knowledge Management Framework
Leadership Apply Adapt Create Organisational Knowledge Measurement Culture Identify Share Organise Acquire Technology KNOWLEDGE MANAGEMENT ENABLERS KNOWLEDGE MANAGEMENT PROCESSES C. O’Dell & C.J. Grayson If We Only Knew What We Know: Identification and Transfer of Internal Best Practices. Best Practices White Paper, American Productivity & Quality Center, 1997.
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National R&D Technology Components Development Framework (Mainly AI)
Knowledge Management Organization Identification Creation Acquisition Dissemination Adaptation Application Knowledge/ Ontology Multimedia Raw Data Multimedia Knowledge Representation Knowledge Base Frameworks Meaning (e.g. Discourse, Lexicontology analysis) Features (e.g. Phrasal & Dependency analysis) Generic AI Processors Database/XML Multimedia Knowledge Extraction/ Generation Text Speech Image Music Audio Biosignal Graphics/Animation Data/Text Mining Clustering/ Classification Multimedia Data Indexing Focused Information Retrieval Search Pattern Identification Biometrics Distributed computing Parallel computing Grid computing High Performance Computing Ubiquitous computing Universal Multimedia Filter Data Compression Multimedia Communication Data Security Multimedia Network protocol Signal processing Robotic Navigation Video/Stereo Vision Informatics Surveillance Prediction Scheduling Optimization Creative Multimedia Medium Content User Environment Domain specific Applications Healthcare Language Finance Law Robotics Smart office Smart Home Smart Meeting Room Computer Aided Translation 3D Games Culture & Heritage Education 3D Film/Movie Multilingual Translation Question Answering Multimodal Visualizer National R&D Technology Components Development Framework (Mainly AI) KBS Simulation & Visualisation Computational Intelligence Data Mining AI Image Proc Knowledge Acquisition Methods
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UNIT 1 – Introduction to Knowledge Acquisition
Knowledge Acquisition (KA) Methods of KA
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UNIT 1 – Introduction to Knowledge Acquisition
Knowledge Acquisition (KA) includes the elicitation, collection, analysis, modelling and validation of knowledge for the representation of knowledge to be used within intelligent systems WISDOM Understanding relations Understanding patterns Understanding principles Knowledge is a combination of information, experience and insight that may benefit the individual or the organisation – e.g. "When crude oil prices go up by $10 per barrel, it's likely that petrol prices will rise by 10 sen per litre" is knowledge. KNOWLEDGE Information is data that has been interpreted so that it has meaning for the user – e.g. "The price of crude oil has risen from $70 to $80 per barrel" gives meaning to the data and so is said to be information to someone who tracks oil prices INFORMATION Data is unprocessed facts and figures without any added interpretation or analysis – e.g "The price of crude oil is $80 per barrel." DATA Data – symbols <history> Information – answers to "who", "what", "where", and "when" questions <context> Knowledge (& Understanding) – answers to “how’ and “why” questions <present> Wisdom – evaluated understanding <future> IMPLICIT/ TACIT
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Knowledge Acquisition (KA) is the transfer and transformation of potential problem solving expertise from some knowledge source to a program KNOWLEDGE Knowledge Acquisition is a large process itself and is composed of four main stages: Identification- Identifies the knowledge sources and problem characteristics Representation Conceptualisation - Finds concepts to represent the knowledge Formalisation - Designs the structure to organise the knowledge Implementation - Formulates rules, frames, etc., to represent the knowledge Acquisition process – Determines the method to extract the knowledge Testing - Validates the representation that organises the knowledge INFORMATION DATA IMPLICIT/ TACIT identify represent acquire test
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Targeted Representations of Knowledge
Ontologies Rules Semantic networks Frames …… KNOWLEDGE Sources of Knowledge (Identification) Humans (memory) Implicit (not yet documented) Tacit (hard, possibly impossible to describe) Explicit knowledge (raw) Text Images Audio Video Multimedia Partially processed knowledge (Information) Data bases …. INFORMATION DATA IMPLICIT/ TACIT identify represent acquire test
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Acquisition process – How can it be achieved??? Testing Manual
Knowledge Elicitation: Series of interviews between the domain expert and the knowledge engineer who then writes a computer program representing the knowledge Based on interview Track reasoning process Observation Semi-Automated Interaction between a domain expert and a computer program Automated methods (see below) aided by human experts Build base with minimal help from knowledge engineer Allows execution of routine tasks with minimal expert input Automated Analysis technologies (language, speech, image processing, …) Data-driven techniques (computer programs that create associations using a large body of case data) KNOWLEDGE INFORMATION DATA IMPLICIT/ TACIT Testing Surveys …… identify represent ACQUIRE test
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One of the most basic forms of Knowledge Acquisition is Requirements Analysis
[as in Systems Analysis and Design] Requirements analysis in systems engineering and software engineering, encompasses those tasks that go into determining the needs or conditions to meet for a new or altered product, taking account of the possibly conflicting requirements of the various stakeholders, analysing, documenting, validating and managing software or system requirements KNOWLEDGE INFORMATION DATA IMPLICIT/ TACIT
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Knowledge Acquisition (basic)
EXPLICIT WISDOM KNOWLEDGE Data Structured Data (Databases) Data Input (usually manually) Requirements Analysis INFORMATION DATA Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) IMPLICIT/ TACIT Unstructured Data (e.g. numbers) RAW IMPLICIT/ TACIT
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Unstructured Data (e.g. text)
Data – symbols Information – answers to "who", "what", "where", and "when" questions Knowledge (& Understanding) – answers to “how’ and “why” questions Wisdom – evaluated understanding Knowledge answers to “how’ and “why” questions, which requires reasoning, and as such information has to be further processed to allow for this, hence requiring more complex representations. This processing can be manual, semi-automated or automated. Domain Ontology General Ontologies Semantic Networks Cases KNOWLEDGE Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Unstructured Data (e.g. numbers) Data Structured Data (Databases) INFORMATION DATA IMPLICIT/ TACIT
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Knowledge Acquisition Methods
Objectives Unit 1 Introduction to Knowledge Acquisition Knowledge Acquisition (KA) Methods of KA To describe the methods used for knowledge acquisition and knowledge discovery To understand data and knowledge representation in knowledge acquisition To assess the applicability of machine learning algorithms for a specific data mining task To understand various approaches of knowledge acquisition from experts Unit 2 Data Input and Knowledge Representation Types of data input Knowledge representation Reasoning Unit 3 Manual Knowledge Acquisition Requirements analysis Knowledge Engineering Tacit Knowledge Unit 4 Semi-automated Knowledge Acquisition From workflow systems Prototyping Data Mining Unit 5 Automated Knowledge Acquisition Machine Learning: Supervised and unsupervised learning Natural Language Processing Image Processing
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Knowledge Acquisition thus includes:
EXPLICIT Knowledge Acquisition thus includes: Knowledge Engineering (more advanced) Requirements Analysis (basic) WISDOM Domain Ontology General Ontologies Semantic Networks Cases Knowledge Engineering KNOWLEDGE Data Structured Data (Databases) Data Input (usually manually) INFORMATION DATA Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) IMPLICIT/ TACIT Unstructured Data (e.g. numbers) RAW IMPLICIT/ TACIT
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Unstructured Data (e.g. text)
Complex Simpler Intelligent Programmable Integrated Controller(PIC) Reinforced Learning Hidden Markov Models Multilayer Perceptrom Decision Trees Clustering Multivariate Methods Parametric Methods Bayesian Decision Theory Machine Learning EXPLICIT Knowledge Acquisition also includes: Machine Learning (more advanced) WISDOM Semantic Networks General Ontology Ontologies Domain Ontology KNOWLEDGE Cases Structured Data (Databases) Data INFORMATION Data Data Input (usually manually) Data Mining / Machine Learning DATA Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Unstructured Data (e.g. numbers, vectors) RAW IMPLICIT/ TACIT
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Knowledge Acquisition also includes:
STATISTICAL METHODS DATA MINING Processes QUERY INFORMATION RETRIEVAL DECLARATION COMPLEX SIMPLE Balance of Complexity of Processes vs Data COMPLEX Knowledge Acquisition also includes: Language Processing (more advanced) …. Some balance is also required … WISDOM RAW Annotated Morphology Structural Logical Semantics Discourse Dependencies Semantic Networks KNOWLEDGE Ontologies Cases Structured Data (Databases) INFORMATION DATA Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Unstructured Data (e.g. numbers) RAW
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Summary & Generalities
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Knowledge Acquisition is a large process: Identification
UNIT 1 – Summary Knowledge Acquisition is extracting and representing knowledge within the given spectrum WISDOM Understanding relations Understanding patterns Understanding principles Data – symbols <history> Information – answers to "who", "what", "where", and "when" questions <context> Knowledge (& Understanding) – answers to “how’ and “why” questions <present> Wisdom – evaluated understanding <future> KNOWLEDGE Knowledge Acquisition is a large process: Identification Representation Acquisition process Testing INFORMATION Sources (Identification) Humans (memory) Explicit knowledge (raw) Partially processed knowledge (Information) Representations of Knowledge Ontologies Rules Semantic networks Frames …… DATA Knowledge Acquisition Process Manual Semi-Automated Automated Techniques/ Methodologies Knowledge Engineering Machine Learning Natural Language Processing …… IMPLICIT/ TACIT
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Test Represent Acquire Identify Knowledge Acquisition – Essence
DATA INFORMATION KNOWLEDGE WISDOM IMPLICIT/ TACIT EXPLICIT Understanding relations Understanding patterns Understanding principles Knowledge Engineering Identify Represent Acquire Test Domain Ontology General Ontologies Semantic Networks Cases Data Structured Data (Databases) Data Input (usually manually) Requirements Analysis Data Mining / Machine Learning RAW Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (e.g. numbers, vectors) IMPLICIT/ TACIT
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Knowledge Acquisition – Essence
DATA INFORMATION KNOWLEDGE WISDOM IMPLICIT/ TACIT EXPLICIT Understanding relations Understanding patterns Understanding principles This course may appear very complex, but the main points to be learnt are: RAW Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (e.g. numbers, vectors) Attain comfort level with different types of input data. Domain Ontology General Ontologies Semantic Networks Cases Know how to (manually) represent knowledge – Ontology-based Have an appreciation of how knowledge is acquired. Knowledge Engineering Identify Represent Acquire Test
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ULTIMATE – Upgrading Database or Information Systems Applications
The ultimate in Knowledge Acquisition is that knowledge can be automatically collected (e.g. from texts, images, audio, video, …), analysed, modelled, validated and then built into intelligent systems …. Data TYPICAL APPLICATIONS Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (Databases) Data Input (usually manually)
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ULTIMATE – Knowledge Application Builder Knowledge Applications
Ontologies Domain Ontology General Knowledge Base Automated Information literacy also is increasingly important in the contemporary environment of rapid technological change and proliferating information resources. Because of the escalating complexity of this environment, individuals are faced with diverse, abundant information choices – in their academic studies, in the workplace, and in their personal lives. Information is available through libraries, community resources, special interest organizations, media, and the…. Unstructured Data (e.g. text) Structured Data (Databases) Knowledge Application Builder Knowledge Management Engines ULTIMATE – Knowledge Application Builder Knowledge Applications
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Knowledge Representation Fundamentals
UNIT 2 – Summary Knowledge Modelling Knowledge TYPES Needed Usual Declarative Procedural Tacit Explicit Generic Specific Meta-knowledge Knowledge OBJECTS General Specific Concepts Instances Processes Attributes Values Relationships Rules Knowledge OBJECTS ++ Description Situational Relations Knowledge Representation Fundamentals Formalisms Ontologies Others Rules Semantic Networks Frames …. Reasoning Inheritance Chaining (Rules) …..
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THANK YOU MERCI GRAZZIE GRACIAS SPASIBA DANKE MANGE TAK NAN DHRI CAM ON TERIMA KASIH ARIGATO/ OKINI GO MA SSEUM NI DA SHUKRIYA XIE-XIE NI KAMSIAH / MMKOI JABAI INAU NGGO BUTE KABU KOP KUN KAH
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