A Blockchain Cloud Computing Middleware for Academic
Manuscript Submission
ALEXANDROS GAZIS1,*, GIORGOS ANAGNOSTAKIS2,
STAVROS KOURMPETIS3, ELEFTHERIA KATSIRI1,4
1Democritus University of Thrace, School of Engineering, Department of Electrical and Computer
Engineering, Xanthi, 67100, GREECE
2University of Piraeus, School of Information and Communication Technologies, Department of
Informatics, Piraeus, 18534, GREECE
3National and Kapodistrian University of Athens, School of Law, Department of Law, Athens, 10679,
GREECE
4Institute for the Management of Information Systems, Athena Research & Innovation Center in
Information Communication & Knowledge Technologies, Marousi, 15125, GREECE
Abstract: - One of the most important tasks in scientific publishing is the articles’ evaluation via the editorial
board and the reviewers’ community. Additionally, in scientific publishing great concern exists regarding the
peer-review process and how it can be further optimised to decrease the time from submission to the first
decision, as well as increase the objectivity of the reviewers’ remarks ensuring that no bias or human error
exists in the reviewing process. In order to address this issue, our article suggests a novice cloud framework for
manuscript submission based on blockchain technology that further enhances the anonymity between authors
and reviewers alike. Our method covers the whole spectrum of current submission systems capabilities, but it
also provides a decentralised solution using open-source tools such as Java Spring that enhance the anonymity
of the reviewing process.
Key-Words: - Peer review, manuscript submission, academic publishing, journal publishing, scientific
publishing, Blockchain, Blockchain application, education, research, publication, middleware
Received: April 14, 2021. Revised: January 7, 2022. Accepted: January 25, 2022. Published: February 9, 2022.
1 Introduction
In order to publish a scientific article, regardless of
the field, the typical process consists of either
contacting the editor of a journal or simply
submitting a manuscript via a web platform.
Afterward, the editorial board checks the submitted
article to address whether it is within the scope
following author guidelines. Specifically, the board
first validates that the content is original and that its
subject is appropriate for publication in the
submitted article. Second, it checks that the
submitted document (usually in word or latex
format) complies with the described specifications
of the journal in terms of font size, spacing,
paragraphs/equations/figures, and numbering etc.
Then, upon the editorial approval, the article is sent
to a pool of available reviewers based on its content
and their expertise. Last, reviewers send a detailed
report to the assigned editor with their comments
and suggestions regarding the acceptance or
rejection of the article. This report usually consists
of a detailed list of the necessary article revisions
from spotted typos, to sections that need to be
rewritten for clarification, to general remarks on the
logical flow of the text.
The aforementioned process is the current status
quo in academia as it provides an independent and
fast way to assess an article, i.e. author submissions.
Unfortunately, one of the problems with this method
is that it does little to address what is referred to as
“publication bias” [1]. Analytically, this term refers
to the human factor in academic publishing, i.e. the
reviewers who are tasked to act as referees and
argue whether an article shows merit or else provide
a detailed response of possible ways to ameliorate
the articles’ current form. There are many causes for
dissatisfaction in peer-reviewing from over
criticising the results of a specific section, over
addressing issues to delay a competitive research
group, or simply the reviewers undergoing a
stressful period and not providing valuable remarks.
Regardless of the reason, in order to eliminate
human bias, publishing companies have made great
efforts to introduce anonymity when an article is
reviewed. More specifically, the authors rarely
know what the reviewers’ names are and typically,
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Alexandros Gazis, Giorgos Anagnostakis,
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the same applies to the reviewers. In the next
sections, we will represent the types of peer-
reviewing, however, the key point in peer-review is
that in the last years emphasis has been given to
total anonymity for both sides alike.
2 Aims and Objectives
This article aims to introduce a scientific manuscript
submission platform, similarly with [2], [3], [4]
focusing on anonymity. Specifically, this article
aims to introduce a cloud-based privacy-focused
decentralised submissions system that leverages
blockchain technology. First, we provide a brief
literature review on manuscript submission
solutions focusing on blockchain decentralised
applications in education and academia. Second, we
explain the different types of peer-review processes,
their key differences, and the problems associated
with the current evaluation criteria in scientific
publication. Third, we consider how blockchain
technology is used in a number of real-case scenario
applications and provide key points regarding its
architecture. Fourth, we present a novel cloud-based
framework implementing blockchain architecture
for academic article and conference proceedings for
a cloud-based submission system. Last, we draw
conclusions on how to change the current status quo
in order to increase the quality of peer-reviewing
and reduce the reviewing time from submission to a
final decision.
The objective of this work is to showcase the
software design architecture of our middleware
layers and present in detail our benchmark results
regarding the blockchain implementation of our
system. Our proposed system harnesses the
advantages of blockchain architecture to provide a
fast, reliable, and anonymous academic submission
system that further enhances the double-blind
review process as well as provide more accurate
reviewers’ suggestions.
3 Related Work
In recent years, manuscript submission systems
have been intensely studied in various scientific
fields to explain and understand different existing
processes and publication procedures [5].
Specifically, current research trends focus on
explaining the properties of the used platforms as
well as providing all the necessary author/article
affiliation information during an article submission
[6]. In this study, we have examined a recent
bibliography regarding the pros and cons of the
peer-review part of a system and its limitations [7],
[8]. Although peer-review is considered highly
important in article assessment [9], many problems
occur in terms of biases from reviewers and editors
alike [10], [11], [12]. Researchers argue that a great
deal must change in order to enhance current
procedures as mentioned in [13], [14]. Moreover,
emphasis is given on technical aspects of peer-
review [15] such as policymaking processes [16],
enhancing anonymity over transparency [17], and
reliability and quality of peer-review [18].
One of the most interesting aspects in
manuscript submission systems is arguably the
human factor and how human assessment can be
subjective and prone to bias [19]. The latest research
trends have recommended new ways to eliminate
human bias via the use of AI [20], Big Data analysis
[21], game theory analysis [22], statistics [23], new
evaluation models [24], strategies [25], and
algorithms [26]. Analytically, one of the most
promising technologies regarding eliminating
biases, promoting objectivity, and anonymisation in
peer-review is blockchain technology [27], [28].
This technology is used for various reasons such as
detecting plagiarism [29], shared governance in
publishing [30], fairness evaluation via permission
checks [31], and web-based file-sharing systems
[32].
Finally, it is noted that since blockchain
promotes decentralised applications, many
frameworks leveraging its properties in academic
services exist with high security features due to its
anonymous nature [33], [34], [35], [36].
Specifically, similarly to the framework presented in
this publication, this paper is influenced by the
following studies regarding “all batteries included”
software solutions in scientific publication
submission systems [37], [38], [39], [40], [41], [42].
4 Background and System Properties
Current scholarly publishing consists of the
following stages [43], [44]:
Registration: providing an official timestamp
of submitted scientific results.
Certification: peer-reviewing to access and
validate scientific discoveries.
Dissemination: distribution of discoveries to
the academic community.
Preservation: digital/physical storage of a
publication or data(set).
In this article, we provide a tool regarding the
second stage of publishing, i.e. the certification.
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This process consists of peer-reviewing. It is the
only universally accepted method for validating and
accessing the quality of articles and conference
proceedings.
4.1
Peer-Review Process
Peer-reviewing of academic works dates back to
1731 by the Royal Society of Edinburgh, which
adopted an editorial procedure for a collection of
peer-reviewed medical articles and subsequently
became a norm after World War II [45].
“Reviewers” are the cornerstone of academic
evaluation in communities as they act as experts in a
field to -pro-bono- read, understand, access, and
validate the findings of their peers. Their work
consists of examining a publication and providing
valuable insights to an editorial board that, based on
their opinion, decides to accept or reject a
manuscript as well as provide suggestions to
enhance the content.
The overall peer-review process starts when an
author submits an article to a journal or a conference
for publication. The editorial board then assigns an
editor who performs the initial screening of the text
to assess whether it is within the scope of the
journal’s subjects, aims, and objectives. Later, if the
editor does not decide that the publication has
scientific value, he/she rejects it (desk reject), or
otherwise, he/she contacts reviewers (typically 3-6
persons) to provide their remarks on the submitted
publication. After that, when all the reviewers
answer and provide their insights as well as
suggestions for publication (accept/reject etc.), the
editor takes all of their reports under consideration
and provides them to the author(s). This is the final
round of reviewing and it usually consists of
communication between the author(s) and the
reviewers where all their comments are addressed
and the article is resubmitted, reread, and re-
evaluated.
Last, if reviewers state that no further actions are
requested by the authors, the article is accepted and
it is considered for publication. This process is
briefly presented in Figure 1 [46]:
Fig. 1: Diagram of Peer-Review process
4.2
Peer-Review Types
In this section, we explain what the types of peer-
reviewing are and their unique characteristics.
Analytically, peer-reviewing can be categorised into
the following:
Single-Blind: where the reviewer(s) identity
is hidden from the author(s).
Double-Blind: where both the author(s) and
the reviewer(s) identity remain hidden.
Open: where both the author(s) and the
reviewer(s) identity are known to all available
parties.
Transparent: where the review(s) are posted
alongside an article but the reviewer(s) retain
the right to hide their identity.
The cloud-based application and the algorithm
we propose in the following sections focus on
expanding the double-blind review category.
4.3
Blockchain Architecture
The most famous and burning example of
Decentralised Ledger Technology (DLT) is
undoubtedly blockchain technology. Blockchain is
simply a public ledger that keeps all the transactions
of its users in a validated and permanent manner,
without the need for a centralised authority or
intermediaries. Blockchain is similar to databases.
However, what makes blockchain different is the
fact that the validation is a job of existing nodes and
not of a central authority.
Keywords of this DLT are nodes, miners,
blocks, and cryptography, and in the following each
one will be explained while describing the
procedure of blockchain [47].
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4.4
Blockchain Key Points
Blockchain is a public ledger because all the nodes
existing in it are always of equal value and
importance (p2p). As obvious from its name,
blockchain consists of x blocks in a chain. Each
block consists of inputs (e.g. transactions) and when
a block has stored the required amount of data it is
added automatically to the chain of blocks. From the
moment that a block is added to the chain, there is
no chance of changing the data, since all the data
has been distributed to all the nodes [48]. This
makes blockchain technology decentralised and
distributed meaning that this ledger is kept at the
same time in the same way by all its nodes. The
distribution of every new input to the ledger and the
addition of every new input to a block - after a first
validation (checking the legitimacy of the inputs) -
is the work of what are called miners. Miners are
individuals who use advanced computing programs.
Specifically, they are rewarded for every block they
form for their services (i.e. computational processes)
[49].
This ledger must be credible and trustworthy. It
is clear that allowing certain individuals, meaning
the miners, to add blocks to the ledger and get paid
for every block they add, creates conflict of
interests. This is because every miner is motivated
to execute the job in a fast and not so responsible
way. Imagine the case where two miners find the
same transaction and at the same time add to their
own block the very same transaction. The danger of
double-written and stored inputs means that the
user, who is the debtor, would see that he/she has a
debt double the size of the one agreed to be paid.
For this reason, it is certainly necessary for a
consensus protocol that clarifies the way that all
proofs are validated and the way that every block is
created and stored in the system. Analytically, a
consensus mechanism -which consists of a complex
algorithmic process - is a set of rules that decides on
the contributions by various participants of the
blockchain. Moreover, the two most common
algorithms are proof of work (PoW) and proof of
stake (PoS). PoW requires participant miners that
the work that was done and was submitted by them
qualifies them to receive the right to add a new
transaction to the chain. PoS involves the allocation
of responsibility in maintaining the ledger to a
participant miner in proportion to the number of
virtual currency tokens held by it [50].
Last but not least, anonymity is another
characteristic of blockchain as all inputs are
delivered through the use of code names (a unique
digital signature).
5 Proposed System
In this section, we provide a top-down approach to
our system. First, we present our middleware
architecture, emphasising the layers we have chosen
to develop in order to achieve optimal execution.
Second, we present the algorithm of our application
and provide a step-by-step explanation of its
rationale. Third, we briefly present our blockchain
implementation. Fourth, we illustrate in detail the
entities of our application and present the system
properties where this application was executed.
Last, we showcase our results and discuss our
findings for low to mid-size application sizes (based
on the blockchain prefix value i.e. complexity).
5.1
Middleware Proposed
Our middleware has a four-tier architecture as it
consists of the following layers:
Infrastructure layer: consisting of our
Raspberry Pi devices and a high-end
computer.
Network layer: consisting of the network
properties responsible for generating, storing,
and maintaining our blockchain architecture.
Common Services layer: implementing
services to create and update our system and
via the search and validation process to
propose reviewers.
Application Services layer: parsing the
reviewers’ list after successfully executing
our proposed algorithm, sending the email
invitations, and logging the reviewers’
responses and time frames for submitting
their reviews.
Moreover, regarding the application services
layer, we have expanded on the work of [51] to
ensure that each computing device could act both as
a server and a client. Analytically, we emphasise
this attribute as it provides a system without a single
point of failure as well as presenting a cloud-based
application where each computer can switch roles
between server-client. This would thus ensure
“continuity of operation”. Last, we notice that the
common services layer encapsulates the business
logic of our application whereas the application
services layer acts as a level of abstraction for our
application default APIs execution.
As evident from the above, these two layers are
closely connected as there is not a clear separation
of concerns. However, we have chosen to split the
application’s stage into two layers in order to
address future issues regarding scalability and the
system complexity rapidly increases.
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5.2
Algorithm Proposed
In this section, we present the proposed algorithm of
this article used to develop our database for
proposing reviewers and facilitating a submission
process. Specifically, we aim to develop a system
that would be characterised by a high fairness index
for both authors and reviewers alike. Analytically,
one of the most common issues in peer-review that
slows down the review process and first-time
decisions is undoubtedly selecting inappropriate
reviewers. This is usually achieved either by
sending an invitation to review to people with a
"conflict of interest" or constantly providing an
invitation for specific publications to the same
reviewers' target group. In order to avoid that, we
have developed an algorithm that took into
consideration these issues and focused on providing
an optimal solution.
More specifically, we have developed a Java-
based system that comprises of 3 stages to optimally
search and propose the names of possible review
candidates. First, we used several "filterReviewers"
functions that mapped the most suitable candidates
for review into a list. Analytically, this was achieved
by indexing, storing, and searching a preset dataset
of keywords in a database. This constructed DB
accumulated information from all the submitted
articles and their metadata (titles, keywords,
abstracts) as well as other general information (other
articles, research interests, etc) which were provided
by their profiles on academic social networking sites
such as ResearchGate, Academia.edu, and other
academic open-source services such as Publons as
well as ORCID profiles. Last, after gathering all the
necessary data, we generated a hash and stored them
using blockchain technology to ensure anonymity as
well as continuity of operations while monitoring,
notifying, activating, and deploying all the
necessary processes regarding authors-reviewers.
Second, after filtering and mapping the
necessary information to our DB, we monitored our
output result to ensure that the reviewer was not a
past co-author and that added a lower priority index
for reviewers who have answered or declined our
previous requests in an effort to further decrease the
duration of the process. This stage ensured that the
submission system did not waste time proposing
reviewers who had declined to review as well as
categorise lower in the list of our proposal the
reviewers who had already reviewed (and rejected)
an article.
Last, in the final stage of our algorithm, the
system sends email invitations over a time frame of
1 week for the candidates to express their interest
and respond positively or negatively (no answer is
considered as declined). Initially, we sent 6 email
invitations as we consider a minimum of 3 and a
maximum of 6 reviews adequate to evaluate a
manuscript. In the case of a positive answer, the
system awaits the reviewers' remarks for 4 weeks.
The outline of our algorithm is described in Table 1.
Table 1. Algorithmic solution for reviewers’
selection and proposal
STEP 1:
Initialise necessary variables.
1.1
Select x //article for review
1.2
Input y, min(3), max(6)
//number of reviewers we want (3≤y≤6)
1.3
Z = filterReviewers()
// find suitable candidate reviewers for the
article by using crawlers to match keywords of
already reviewed articles and ORCID records
1.4
SR list= empty
//create an empty list of the selected
reviewers (SR)
STEP 2:
Find y reviewers
2.1
While SR.length < y
2.2
Select candidate reviewer from z
If (candidate reviewer is the author of
the article) Then
Remove reviewer from Z list
Reject candidate reviewer
GoTo step 2.2
Else If (reviewer has negatively
reviewed the article in the past) Then
Flag candidate reviewer as a low
priority for this article
Reject candidate reviewer
Go-To step 2.2
Else Add to list SR
End-of-while
STEP 3:
Send article to selected (SR) reviewers
STEP 4:
Create block
STEP 5:
Add block to the blockchain
STEP 6:
Save blockchain
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Fig. 2: Entities UML diagram of our proposed middleware architecture
5.3
Blockchain Application Implementation
A blockchain is a digital ledger of transactions that
is duplicated and distributed across an entire
network. It consists of several different blocks each
containing a timestamp, the cryptographic hash of
the previous block, and the transaction data of our
choice. Analytically, the timestamp property assists
the computer network in calculating the
computation length to adjust the mining difficulty.
The difficulty is measured by the total time duration
to create a block while, simultaneously, containing
the previous block hash information. As a result, a
blockchain that is resistant to modification is created
where it will be invalid if only one block is altered.
Furthermore, blockchain is completely different
from typically centralised databases. Specifically, in
a blockchain, every participant within a network
maintains, approves, and updates the new blocks.
This results in developing a decentralised
architecture maintained by both publishers and
reviewers alike. This feature is of great importance
as it allows all the parties inside this p2p network to
constitute a node that can confirm and validate the
order of our linked list of blocks. Last, the authors
emphasise that the architectural design principles
regarding a private blockchain architecture dictate
that only specific organisations and authorised users
can ensure the immutability, anonymity, and
security of a blockchain.
5.4
System Properties - Experimental Setup
In this section, we provide information regarding the
proposed system hardware and software properties.
Specifically, during our tests, we used the Raspberry
Pi Model 3b-4-4a and other low-power and low-cost
devices running on Unix operating systems. More
specifically, the Raspberry Pis installed operating
system was Raspbian or New Out Of The Box
(NOOBS). Moreover, we used a high-end computer
as a server coordinating and orchestrating the
middleware and the execution of the algorithm with
the following specifications: architecture x86-64 bit,
Windows 10, AMD Ryzen 7 3700x 8-Core
Processor at 3.58 GHz, RAM 16.0 GB, SSD
Kingston Electronics Disk 256 GB. Last, Java is
executed with the following specifications: tomcat
version: 2.4.1 and the packages used are the
following: Hibernate,
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Java JDBC API, MySQL Connector and Java Util
package.
A flow chart representing the entities of the
proposed architecture is presented in Figure 2.
Analytically, in this class diagram we showcase the
roles of the user who can act as a reviewer, a
publisher, or both. Moreover, we emphasise the
article entity which illustrates what information is
crawled and indexed for a document such as its
status (published, rejected) and the date/time it was
submitted, etc. Furthermore, as evident from our
class diagram, a user has a direct relationship with
the article entity based on their common properties.
First, a many-to-many relationship exists between
the articles a user has written and the opposite, i.e.
the users who are the authors of an article. Second,
between review and user entity occurs as a user may
review none or many articles (a fact that we are not
able to detect through this relationship). Last, the
review entity stores all the information regarding
validations, proposing and selecting the most
suitable reviewers, as well as several parameters for
safekeeping the review procedure integrity.
5.5
Application Results
This section focuses on presenting the results of our
benchmarks regarding our simulation and
specifically, the prefix value of our blockchain
architecture. Analytically, increasing the zeroes
value of the prefix to the required block hash makes
it more difficult to generate it.
Specifically, this problem occurs because not
only does it consume more memory but, as evident
from our tests, the execution time rapidly increases.
In Figure 3 and Figure 4 we have graphed the mean
creation time and the mean request number (tries)
for a prefix value up to 6. As evident, the hash value
increases exponentially for prefix numbers greater
than 5 where the trade-off between generating
encrypted data points and execution time / available
computer resources is inadvisable.
Moreover, in Figure 5 we illustrate the results of
Table 2 in which we notice that CPU usage does not
fluctuate. Specifically, this occurred during all our
tests for various prefix values whereas, during our
high-end computer tests, generating the hash
consumed on average 7% of the total CPU power on
an 8 core 16 threads CPU device using only 1
thread to generate the hashes.
Last, with regard to memory usage, from our
tests we concluded that it is tightly connected with
the prefix value as it gradually increases its value
to enable the JVM to efficiently take advantage
of the available system process for generating block
hashes.
Table 2. Hardware benchmarks regarding the prefix
value computation
Prefix value
RAM
CPU
[%]
Mean Time
[mins]
2
0.75
6.60
0.02966
4
1.20
6.80
0.19000
5
1.23
6.90
0.46000
6
2.00
7.12
7.20000
Fig. 3: Mean execution time for prefix creation
Fig. 4: Mean number of requests for different prefix
values
Fig. 5: JVM benchmarks regarding CPU, RAM, and
mean execution time for different prefix values
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6 Conclusion
In this article, we have developed a cloud-based
application that cold counts, tracks, and monitors an
academic reviewing system process. Specifically,
the results of our study for a set of random
generated data parsed from internet bots (web
crawlers) were promising, which is why we propose
expanding this application to real word case
scenarios. This means that it could be used as a
submission system for a medium to small-size
international conference submission system.
Furthermore, as evident from our benchmarks, this
application can be implemented in Raspberry Pi
computers i.e. into several portable low-power and
low-cost computing devices capable of supporting
our system without a “single point of failure”. This
occurs as all available computers can act both as a
server and a client, thus providing the necessary
information and statuses to the blockchain.
As for future works, our system’s architecture
should be expanded to a multithread application
which would substantially increase the hash
generation uptimes for all the available devices in
our computer network. Moreover, we are planning
to open-source our APIs for other online submission
systems to be able to obtain information on our DB
of reviews. Last, since most of the existing
submission systems are written in PHP and not Java,
we plan on expanding our APIs implementation and
packages to other computer programming
languages.
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93
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Alexandros Gazis, was responsible for
conceptualization, investigation, methodology,
software, validation, visualization, writing the
original draft, review-editing resources and carried
out the simulation and the optimization and writing
original draft.
Stavros Kourmpetis, was responsible for
investigation, methodology, resources, validation,
writing the original draft, review, and editing.
Giorgos Anagnostakis, was responsible for
investigation, methodology, resources, validation,
visualization, writing the original draft, review, and
editing. Moreover, he has organized and executed
many of the experiments of Section 5 and helped
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.51
Alexandros Gazis, Giorgos Anagnostakis,
Stavros Kourmpetis, Eleftheria Katsiri
E-ISSN: 2224-2899
571
Volume 19, 2022
greatly in the software development (the Java
implementation) of the proposed middleware.
Eleftheria Katsiri, contributed to the
conceptualization, data curation, formal analysis,
funding acquisition, investigation, methodology,
project administration, resources, software,
supervision, validation, visualization, writing the
original draft, review, and editing.
Sources of Funding for Research Presented
in a Scientific Article or Scientific Article
Itself
Not applicable
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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DOI: 10.37394/23207.2022.19.51
Alexandros Gazis, Giorgos Anagnostakis,
Stavros Kourmpetis, Eleftheria Katsiri
E-ISSN: 2224-2899
572
Volume 19, 2022