Ai driven occupational skills generator

E-PROCEEDING OF THE 8TH INTERNATIONAL
CONFERENCE ON SOCIAL SCIENCES RESEARCH 2019
E-PROCEEDING OF THE 8TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES RESEARCH (ICSSR 2019).
(e-ISBN 978-967-0792-36-1). 18-19 November 2019, Imperial Heritage Hotel, Melaka, Malaysia.
Organised by https://worldconferences.net Page 29
AI-DRIVEN OCCUPATIONAL SKILLS GENERATOR (AIOSG)
Goon Wooi Kin (wk.goon@mimos.my), Kee Kok Yew (ky.kee@mimos.my), Nazarudin Mashudi
(nazarudin.mashudi@mimos.my), Amru Yusrin Amruddin (yusrin@mimos.my),
Ganesha Muthkumaran (ganesha.muthukumaran@mimos.my)
Enterprise Government Solutions Lab, Big Data Analytics Lab,
Artificial Intelligence Lab & Blockchain Lab,
Corporate Technology Division, MIMOS Berhad
ABSTRACT
The revolution of artificial intelligence (AI) is reshaping the world as we know it in terms of job
inequality and automation. Jobs are changing where the focus is moving more towards skillset rather
than academic qualifications as systems become more intelligent. Therefore, the need for every country
to align the skills development of their workforce towards the progression of technology is of paramount
importance. In this study, the authors present a novel idea and practical methods to capture and process
knowledge and experience in skillset to generate occupational skillset guidelines. This platform, from
here onward referred to as AI-driven Occupational Skills Generator (AIOSG), Malaysia’s applied
research and development center, MIMOS Berhad. AIOSG captures information on occupational
structure, occupational area, competency levels, competency profile, competency based curriculum, and
guidelines for assessment and training to create an occupational skills reference document. This
information is captured from market analysis, human resource departments (Government and private),
industry experts, and Internet literature. At present, such a reference document is produced manually
through conducting workshops involving industry experts. Hence, the document may not include
sufficient inputs from all active practitioners of an occupation, and it is often produced with some level
of obsolescence in that it lags behind current technology and process by the time it is published. AIOSG
leverages on AI’s strength in Natural Language Processing (NLP) and Neural Networks housed in a
web portal built on analytics to capture information from various contributing stakeholders on
occupational skills. The platform then processes relevant information and draws related information in
the creation of ontologies. All relevant information pertaining to a particular occupation is then
structured into a reference document which allows further review and inputs from experts. The platform
finally publishes the final, reviewed version of the document upon approval by the decision-making
authority and records of the data is stored on a blockchain. AIOSG cuts down the time and effort needed
while increasing the accuracy of information for a reference occupational skills document that training
institutes, employers, and employees potentially use to close the gap between the industry and skilled
workforce.
Field of Research: Artificial Intelligence, Occupational Skills, Skillset, Skills Development,
Competency, Training, Natural Language Processing, Neural Networks, Workforce, Human Resources,
Blockchain, Hyperledger, Web Crawler, Analytics, Labour Market
---------------------------------------------------------------------------------------------------------------------------

1 Introduction
An occupational skills reference (OSR) document is a document that provides guidelines on the skill
sets necessary to perform tasks in a particular occupation. The document also provides a description of
the occupation, which is framed from the tasks and skill sets needed. Preparation of the OSR document
requires collection of and review of information with regard to the skillsets required to complete the
tasks under this occupation. The information includes, but is not limited to: interviews and workshops
with industry experts and review of labour market reports, projection reports of labour market demand
and supply, and other literature. With the current pace of advancement of technology and automation,
timeframe for skills relevance and demand for new and enhanced skills, the OSR for various
occupational sectors would need to be updated regularly.
However, the current process of production of an occupational skills reference (OSR) document is
resource-intensive, in terms of both time and cost. Lack of availability of individuals with sufficient
skillsets and well-developed industries with which to use and enhance those skills leads to less relevant
information on skills being captured and utilised for the creation of the OSR document. Conducting a
literature review of the data collected against existing market literature, reports and other pertinent
documents can be costly in both time and human resources. The limited ability of humans to forecast
the skills that may be in demand in the near future, is a shortcoming which may render the document
obsolete in a relatively short period after its publication. With these shortcomings in the current, human
labour-intensive process, process engineering and artificial intelligence would be employed to reduce
the time-to-market and financial cost of the OSR documents.
2 Methodology
This chapter first introduces the AIOSG then explains the system and components of AI-driven
Occupational Skills Generator.
The AIOSG is composed of three components:
1. Analytics in the form of an intelligent web crawler that crawls and analyses the web and specific
government/private databases (which includes information from market analysis, human
resource departments and industry experts) and literature repositories for:
a. information on the skills and competencies needed for a particular job title,
b. market demand for skillsets (existing, developing, non-existent in the current market),
and
c. regular feedback from industry experts on skills needed, both current and future, as
well as skills expected to become redundant.
2. An AI-based service that processes the information obtained from the crawler. The AIOSG
ontology construction comprises curriculum resource acquisition, domain concept extraction,
ontology relation mining, ontology description and ontology updating and leverages on Natural
Language Processing (NLP) and Neural Networks.
3. A blockchain backbone based on Hyperledger stores the records of the occupational skills and
to trace the changes and updates to the records through digital footprints as well as prevent the
records from being tampered.
All data is finally displayed on a visual dashboard for total bird’s eye view of the occupational skills
reference. This is used for decision making, publishing (generated into document format) and future
planning of the governing authority in collaboration with the industry lead bodies. Through this, it
ensures relevant and applicable occupational skills reference are applied and are able to be utilised for
and by the industry.

The overall system for AIOSG is as follows:
Figure 1: AIOSG system architecture
3 AIOSG Analytics (Data Crawler & Analysis)
3.1 Topics Related to Skillset
Topics related to search term, in this case skillsets, contain semantic-related topics that can be used to
narrow down the search result by adding the topic to the initial search term. Figure 2 shows a screenshot
of topics related information in the Keyword Cloud.
Analytics
Layer
AIOSG UI
Presentation
Layer
Blockchain/
Data Layer Blockchain Distributed Records
Artificial
Intelligence
Layer
Integration
Layer Developer API
REST
Data Crawler
Filtering Engine
Relational
Engine
xxx
xxx
xxx
Language
Detector
ANN (SOM)
NLP
Text Extract

Figure 2: Topics related to search term
3.2 Categories Related to Skillset
Categories related to search term contains semantic related categories that can be used to narrow down
the search result by adding the topic to the initial search term. Figure 3 shows a screenshot of categories
related information in the Keyword Cloud.
Figure 3: Other categories related to search term
3.3 Related Skillset Keyword Method
Figure 4 shows the process and flow to generating related keywords from user input search term.

Figure 4: Process and flow to generating related keywords from user input search term
3.3.1 Language Detector
Figure 5 details the process of how these systems perform language detection on the search term:
 User will insert search term in search form. Example: Computer
 Language detector will detect insert search term languages status. Example: Computer
(English), Komputer (Malay).
 Keyword is tagged with identified language.
Search term inserted
Search term tokenized
Each token compared with language service
(Example: Google language detection)
Language
detect service.
Each keyword tagged with its language
Figure 5: Language detector flow
Input:Search Term
Get relatedKeyword Search WWW
Comparecontentsof
search result with related
keywords
Assign frequency to
relatedkeywords
Output3: Topics
relatedto keyword
Output1: WWW Links (Web, Wiki,
News,Books, Blog, Location,
Video,Images)
Output2:
KeywordCloud
Ontology
Wiki
Lexical
Output4: Categories
relatedto keyword
LanguageDetector
KeywordGenerator
3.3.1
3.3.2
3.3.3
3.3.4

3.3.2 Get Related Keyword
Each keyword then will go through the process as shown in Figure 6 to get any possible related keyword
from Wikipedia, Machine Readable Dictionary (MRD) or WordNet based on tagged language:
Token of keyword with tagged language
Each tokenized keyword will query wikipedia page
based on detected language. All hypertext linked word
will be grabbed and store to keywords repository
database.
Each tokenized keyword will query MRD thesaurus,
every synonym word in thesaurus will be stored to
keywords repository.
Each tokenized keyword will query WordNet, every
synonym word in WordNet will be stored to keywords
repository.
Keywords
repository
Figure 6: Get related keyword flow
3.3.3 Search Result Content Compared to Related Keywords
All search results on web content, news, blog, video description, image description from the Internet
search engine using the search term inserted by the user will be stored to temporary database. The
semantic similarity algorithm influenced by Noah et al. (2007) and Li et al. (2006) will aggregate all
stored search results using two-tier aggregation processes as shown in Figure 7:
a. Semantic similarity on WWW title:

Search term WWW Titles
Joint word set
Raw semantic
vector 1
Raw semantic
vector 2
Semantic vector 1 Semantic vector 2
Semantic similarity
index
Wordnet
MRD
Figure 7: Semantic similarity algorithm
• Each web search title will be compared with search term insert by user using the method
proposed by the flow chart in Figure 7.
• Joint word sentence is:
S = S1  S2
= {w1, w2, ……., wn); wi are distinct
Example:
S1: software developer Malaysia (search term by user)
S2: ethical hacker (WWW search title)
S = {Malaysia, software, developer, ethical, hacker}.
S in distinct words generated from combination of S1 and S2.
• Each words in S1 and S2 will be compared with each words in S using
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• Each element in the matrix is compared with:
• where C is the set of unique overlap words found in the meanings of w1 and w2 and M refers
to the meanings of the respective words in WordNet. Therefore, r(C, Mw1) refers to the ratio
between the counts of meanings that contains any of the words in C with all the meaning
associated with w1.
• For the calculation of the semantic vector Si, the following formula is used:
The value of I(w) is calculated by referring to the MRD dictionary, using the following
formula:
• Then, the semantic similarity between the two compared sentences is simply the cosine
coefficient between the two semantic vectors.
• Ss (Semantic value of compared each search term and WWW title) will be stored in database.
Ss = 1.0 meaning search term is same with WWW title semantically else it gives 0 value.
b. Content categorisation:
• Each content of selected WWW title will be categorised using Naive Bayes (“Naïve Bayes
classifier,” n.d.) formula, the probability that a given document D contains all of the words
, given a class C is (Strickland, 2014, p. 75):
The source of set of words are using keywords repository by previous process as shown in
Figure 7.
3.3.4 Assign Frequency to Related Keywords
All top 10 WWW title with highest semantic similarity score and categorised score will be selected.
Figure 8 shows the process to assign frequency to related keywords:
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All words in top 10 selected WWW title content will be
tokenized
Each tokenized words will going thru to tag cloud
creation module
Higher frequency words will represent keywords with
big font in Keyword Cloud.
Finally related keywords is created.
Figure 8: Assign frequency to related keywords
Finally, related keywords from user input search term is created by following the method explained
above and they are listed in the Keyword Cloud section in the AIOSG system. The keywords in the
Keyword Cloud can either be used to initiate a new search or restart the search by adding the keyword
to the initial search term. This will return a narrowed down result, thus making the required information
more easily obtained.
4 AIOSG Artificial Intelligence
In the context of Artificial Intelligence (A.I.) using the ontology matching method is essential, this is
because the model can be grounded on element, structure, instance or multiple strategies (Hu et al.,
2008; Pirro and Talia, 2010; Belhadef, 2011; Liu et.al., 2012). As described in Zhu, Y. C., Zhang, W.,
He, Y., Wen, J.B., & Li, M. Y. (2018), multi strategy method works best because the conceptual
semantics and the hierarchy between concepts are weighted and integrated, while others lack either in
description function of attributes and relations or there is no intersection between the instance sets of
two ontologies.
As the AIOSG ontology is automated using web crawler, text mining and association rule mining,
Following this method, the ontology construction can be divided into five phases: curriculum resource
acquisition, domain concept extraction, ontology relation mining, ontology description and ontology
updating (Figure 9)(adapted from Zhu, Y. C., Zhang, W., He, Y., Wen, J.B., & Li, M. Y. (2018)).

Figure 9: Flow chart of the curriculum ontology construction
4.1 Natural Language Processing (NLP)
In order to upkeep the curriculum for variety of jobs and new skills in the market, the ontology extension
and update of the recent skills and job libraries has to be updated automatically, for which this area shall
require process matching ontology AI that works best. Among all the machine learning approach, there
are two applicable AI approach which are Natural Processing Language (NLP) and Neural Networks,
to be discussed in this paper.
Figure 10: Extracting Information from text and NLP Process
Figure 10 above shows how a web crawler extracts information from text, which also exhibits how
NLP and ontological processing works. Firstly, an ontology can be used directly when building the
lexicon, defining the terms (concepts and relations) for content words. Secondly, an ontology is a
knowledge base, expressed in a formal language, and therefore it provides knowledge for more complex
language processing.

4.2 Neural Networks for Ontology Matching
In the framework for ontology matching, extended model of AIOSG represents the unsupervised neural
network based learning which is suitable to the knowledge structure because it fits the concepts and
relations for content words i.e. taxonomy. This is based on the applicability of a self-organising
map(SOM) or self –organising feature map(SOFM) as a type of artificial neural network(ANN) that is
trained to produce a low dimensional, discretised representation of the input space of the training
samples which is called a map, another method used in dimensionality reduction. The unique approach
in SOM as competitive learning is applied as opposed to error correction learning such as
backpropagation with gradient descent. Based on weight, the neurons are initialised either to small
random values or sampled evenly from the subspace spanned by the two largest principal component
eigenvectors. When a training example is used, its Euclidean distance to all weight vectors is computed,
of which the most similar weights to the inputs will be called best matching unit(BMU). The update
formula for a neuron v with weight vector Wᵥ(s) is
Where s is the step index, t an index into the training sample, u is the index of the BMU for the input
vector D(t), α(s) is a monotonically decreasing learning coefficient; θ(u,v,s) is the neighbourhood
function which gives the distance between the neuron u and the neuron v in steps.
Variables
These are the variables needed, with vectors in bold,
 S is the current iteration
 λ is the iteration limit
 t is the index of the target input data vector in the input data set D
 D(t) is a target input data vector
 ʋ is the index of the best matching unit (BMU) in the map
 θ( u, v, s) is a restraint due to distance from BMU, usually called the neighborhood function,
and
 α(s) is a learning restraint due to iteration progress
Algorithm
1. Randomise the node weight vectors in a map
2. Randomly pick an input vector D(t)
3. Traverse each node in the map
 Use the Euclidean distance formula to find the similarity between the input vector and
the map’s node’s weight vector
 Track the node that produces the smallest distance (node is BMU)
4. Update the weight vectors of the nodes in the neighborhood of the BMU by pulling them closer
to the input vector
5. Increase s and repeat from step 2 while s< λ

5 AIOSG Blockchain (Data Storage)
The data from the Artificial Intelligence block will then be stored in Blockchain, Hyperledger Fabric
(Hyperledger, 2019). It is the nature of blockchain itself where the data will be distributed among nodes
and immutable, which means, the data cannot be changed. This grabbing data from blockchain will
visualise a higher accuracy of outcome. The data entered has to be agreed in a consensus before storing
in the blockchain, which creates trusted data by the experts/AI. Even if a third party wants to change
data, the data will be changed in their own node. This action, however, will fail to add or modify the
information in the blockchain because the blockchain technology will always cross-refer with other
nodes if the data is the same and it will check if the block hash is the same as the previous block, thus
demonstrating immutability.
Figure 11: Representation of a blockchain network
Figure 12: Block containing transactions
Referring to Figure 12, blockchain creates blocks in an append-only structure. This disallows the ability
to delete and update. The data or transactions inside can be updated and will be added as a new block.
Each block has its own hash value before adding onto the chain of blocks.
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
Node 8
Node 7

Figure 13: Links of blocks
Referring to Figure 13, the data hash is linked to each block. This happens when a block is added upon
an agreed consensus. Due to this property of blockchain, if a person tried to edit their data, the block
will change and will not be linked to the previous hash. All nodes the nodes will then cross-refer each
other and check if the data is legitimate. If the data entered is not in concurrence with the data found in
the other nodes, the blockchain will pull the latest correct block from the other nodes.
5.1 Processed Data Stored in Blockchain
The processed skills and competencies data will be stored in the blockchain to ensure the integrity and
security of the original data. Figure 14 shows the process flow of how data is stored in the blockchain.
Figure 14: Data submission to blockchain flow
Processed data captured
using AI
Data is submited via an
invoke call to the
blockchain API
Transaction proposal
submited for consensus
approval
Acquire approval from
consensus and peers
Transaction signed by
peers and submitted for
block creation
Block containing
processsed skills and
competencies data is
created and appended to
the chain of blocks

The processed skills and competencies data will be sent to blockchain API. This will then perform a
transaction proposal submission to the peers in the blockchain network. The peers will then endorse the
transaction proposal and return the simulated transactions and endorsing the peers’ signatures. The
application waits until it receives enough endorsed transaction proposals and will send the endorsed
transaction to the Ordering Peer Service to create a new block and update the ledger.
A smart contract or chaincode is required by the blockchain to store data inside. The following
chaincode shows how the occupation is added into the blockchain.
func (m *AIOSGChaincode) addOccupation(stub shim.ChaincodeStubInterface, args
[]string) pb.Response {
if len(args) != 7 {
return shim.Error("Incorrect number of arguements. Expecting 7 args")
}
// Input sanitation
fmt.Println("- Start Submit Occupation -")
if len(args[0]) <= 0 {// User
return shim.Error("User Required !")
}
if len(args[1]) <= 0 { // Project Name
return shim.Error("Occupation Required!")
}
user := args[0]
occp := args[1]
for tsid := 1; tsid >= 1; tsid++ {
tid := strconv.Itoa(tsid)
idAsBytes, err := stub.GetState(tid)
if err != nil {
return shim.Error("Failed to get state for " + tid)
}
if len(idAsBytes) == 0 {
fmt.Printf("No record found for " + tid + " ! Safe to add !")
occupation := &Occupation{OccID: tid, OccName: occp}
tsJSONasBytes, err := json.Marshal(occupation)
if err != nil {
return shim.Error(err.Error())
}
//Putstate
err = stub.PutState(user, tsJSONasBytes)
if err != nil {
}
// indexed and saved

tsKey, err := getTSKey(stub, occupation.OccID, occupation.OccName)
if err != nil {
return shim.Error("Error getting Occupation key" + err.Error()
)
}
fmt.Println(tsKey)
value := []byte{0x00}
stub.PutState(tsKey, value)
fmt.Println("--- end submit Occupation successfully ---")
} else {
tsid = tsid + 1
}
}
return shim.Success(nil)
}
Figure 15: Smart contract to add occupation
The data is added into a struct. A struct is a structure of how the data captured is stored. Figure 16
shows how the structure of data is stored in blockchain.
type Occupation struct {
OccID string `json:"occID"`
OccName string `json:"occupation"`
Keywords []string `json:"keywords"`
Skillset []string `json:"skillset"`
}
Figure 16: Data structure stored in blockchain
5.2 Digital Footprint Using Blockchain
The processed skills and competencies data that is stored in the blockchain is immutable. This ensures
any illegal updates performed will be disregarded as it cross references with other nodes to check if the
data is the same or not. This is because, if the data is changed, the data has will change too. This will
disconnect from the original chain of blocks itself. This ensures the integrity of the data.
A digital footprint is established using blockchain. Blockchain traces records from the beginning to the
end (Blockchain Network, 2019). This is enabled using the block hash which are chained together.
Using this feature, any data created or updated in the blockchain leaves a trace of who has invoked the
call as it stores the identity of user too. Not only does the blockchain trace the state of the data, it also
traces the person who invokes it.

Figure 17: Digital footprint in blockchain
Figure 17 shows how the blocks are chained together. Each update or adding of data creates a data hash
and stores the person to has invoked the function. This ensures the digital footprint of each processed
skills and competencies data.
5.3 Processed Data Retrieved from Blockchain for Comparison
The processed skills and competencies data that is stored in the blockchain will be retrieved by the
system to be compared with the skills and competencies database of the organisation. Since the integrity
of the processed data is maintained, it will be used to compare with that of the organisation. Figure 18
shows how the data is retrieved from the blockchain for comparison
Figure 18: Process flow of data retrieval from blockchain flow
A data query call is sent to the blockchain API requesting the specific data. This request is then routed
to any of the peers in the blockchain network. The requested query is then searched in the blockchain
network, if it exists or not. If it exists, the verified data is then returned to the application for its use.
A function in the smart contract allows the data retrieval using the occupation keyword. This will return
the latest data related to the occupation. Figure 19: Function to retrieve data from blockchain shows
the smart contract used to retrieve data related to the occupation:
Data is retrieved
using a query call
from the
blockchain API
Query call is
directed to a peer
Data is verified
with signed
blocks
Processed data is
returned to the
application, if
exist

func (m *AIOSGChaincode) searchKeyword(stub shim.ChaincodeStubInterface, args
[]string) pb.Response {
queryString := "{"Keywords":"+"args[0] "+"}"
queryResults, err := getQueryResultForQueryString(stub, queryString)
if err != nil {
}
return shim.Success(queryResults)
}
Figure 19: Function to retrieve data from blockchain
6 Generated Occupational Skills Reference
A sample table of related knowledge and skills generated by the AIOSG system for a particular OSR is
given below. It shall include occupational structure, occupational area, competency levels, competency
profile, competency based curriculum, and guidelines for assessment and training. With more sources
of information from the Internet in the form of unstructured data and Government and private sector
databases in the form of structured data as well as verification and subject matter knowledge from
industry experts and lead bodies, the final output will be generated quickly and more accurately reflect
the actual real-world knowledge and skills.
OSR TITLE Cybersecurity Penetration Tester
REQUIRED
ACTIVITY
RELATED KNOWLEDGE RELATED SKILLS
Manage IP
Network
1.1 Network documentation and change
management
1.2 IP protocol stack layers including:
 Role of a layered protocol stack
 Key functions of each layer of
the IP stack
1.3 Ethernet operation and addressing
structure including:
 Ethernet operating principles
 Ethernet frame structure &
frame fields
 MAC address structure
1.4 Transport layer protocols including:
 TCP and UDP
 TCP flow control
1.5 Network devices including:
 Router
 Switch
 Networking Interface
1.6 IP network management tools and
software
 Protocol analyser
 Command line
1.1 Configuring and operating between
layers of the IP protocol stack
1.2 Interpret Ethernet features and
operations, configuration and
troubleshooting
1.3 Interpret key transport layer protocol
operations and act on them
1.4 Identifying key network devices and
their attributes
1.5 Connecting to key network devices
1.6 Utilisation of suitable tools and
software for managing IP networks

7 Conclusion
This paper presents a system to address the gaps in generating occupational skills reference (OSR)
documents for the workforce. The system crawls for data from the Internet and other relevant databases
in addition to taking account of inputs from industry experts. This is to form a complete picture in terms
of inputs toward a particular job sector. The inputs are then processed in terms of the relative nature of
the skills to a particular job by way of Artificial Intelligence. The processed data is then verified by the
industry expert and lead bodies’ panel to generate the final output. The final OSR is stored in the
blockchain network to ensure traceability in terms of updates of such records and be put up for review
by a panel of experts for decision-making, publishing and future workforce planning. Malicious parties
cannot easily tamper the records of the documents and associated materials, due to the immutable nature
of blockchain. Through the enablement of such a system, relevant and applicable occupational skills
can be utilised by various industries.
8 References
1. Noah, S.A., Amruddin, A.Y., & Omar, N. (2007). Semantic Similarity Measures for Malay
Sentences. [Ebrary version]. Retrieved from
http://books.google.com.my/books?id=8pxUkfwqt_0C&pg=PA117&dq=amru+semantic+mal
ay&source=gbs_toc_r&cad=4
2. Li, Y., Mclean, D., Bandar, Z.A., O'Shea, J.D., & Crockett, K. (2006). Sentence similarity
based on semantic nets and corpus statistics. [Ebrary version]. Retrieved from
http://ants.iis.sinica.edu.tw/3BkMJ9lTeWXTSrrvNoKNFDxRm3zFwRR/55/Sentence%20Si
milarity%20Based%20on%20Semantic%20Nets%20and%20corpus%20statistics.pdf
3. Strickland, J. (2014). Predictive Analytics Using R. Colorado Springs, Simulation Educators.
4. Zhu, Y, C., Zhang, W., He, Y., Wen, J. B., & Li, M. Y. (2018). Design and implementation of
curriculum knowledge Ontology – Driven SPOC Flipped Classroom Teaching Model.
Educational Sciences: Theory & Practice, 18(5), 1351-1374.
5. Hyperledger (2019). Introduction to Hyperledger Fabric. Retrieved from https://hyperledger-
fabric.readthedocs.io/en/release-1.4/blockchain.html
6. Blockchain Network (2019). Retrieved November 2019, from https://hyperledger-
fabric.readthedocs.io/en/release-1.4/network/network.html.

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