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Exploring Academic Integrity in Primary Education: Challenges, Strategies, and Impact
No full textThis study explores promoting academic integrity in primary education, examining its definition, challenges, effective strategies, and implications. Using a mixed-methods approach involving survey and thematic analysis, the study provides insights for educators, policymakers, and stakeholders. Key findings highlight the significance of explicit instruction, clear policies, positive school culture, and parental involvement in fostering academic integrity. Challenges include grade pressure, a lack of understanding, and the consequences of misconduct. Effective strategies involve nurturing critical and creative thinking, leveraging technology, and continuous reinforcement. The study acknowledges limitations, such as limited sample size and reliance on self-reported data, and suggests future research directions, including diversifying the sample and employing multiple research methods. Overall, this paper contributes practical knowledge for promoting academic integrity, supporting ethical development, and facilitating long-term academic success among primary school students
Investigating the Effects of Cloud-Based Business Intelligence Adoption on Services Delivery Quality and Customers Participation in Private Sector Companies in UAE
No full textPurpose: This dissertation aims to investigate the effects of cloud-based business intelligence (BI) adoption on service delivery quality and customer participation in private sector companies in the United Arab Emirates (UAE). The study seeks to address the research gap regarding the specific impacts of cloud-based BI in the UAE context and provide practical insights for organizations striving to improve operations and enhance customer experiences.
Methodology: A mixed methods approach will be employed to collect quantitative and qualitative data from a sample of private sector companies in the UAE. The study will focus on two key areas: the impact of cloud-based BI adoption on service delivery quality and its influence on customer participation. The data will be gathered through interviews and surveys, allowing for a comprehensive analysis of the research questions.
Findings: The findings from this study will shed light on the effects of cloud-based BI adoption on service delivery quality and customer participation in private sector companies in the UAE. The study will provide empirical evidence and insights into the specific benefits and challenges associated with adopting cloud-based BI in the UAE context.
Implications: The results of this study will have practical implications for private sector companies in the UAE seeking to enhance their operations and improve customer experiences through cloud-based BI adoption. The findings will inform strategic decision-making processes and provide guidance for organizations looking to optimize service delivery quality and customer engagement.
Originality/Value: This research contributes to the existing body of literature by addressing the research gap in understanding the effects of cloud-based BI adoption on service delivery quality and customer participation in private sector companies in the UAE. The study provides original insights and empirical evidence specific to the UAE context, offering valuable contributions to the field of cloud-based BI implementation and its impacts on organizational performance and customer engagement
Influence of Firm-Actor Integration on Value Co-creation within Base of Pyramid Market Ecosystems
No full textAcademic and practitioner interest in the very low-income or Base of Pyramid (BOP) markets has been on the rise for the past two decades, resulting in considerable insights into the challenges and opportunities involved in introducing market-based solutions to alleviate poverty among the poorest of the poor in the world. However, in spite of much scholarly work, several facets of the dynamics of strategy implementation and operationalisation at the BOP firm and consumer levels remain obscure. This study aims to illuminate one of these aspects, namely, the influence of the integration of the firm and the multiple actors on the value that is co-created within viable market ecosystems. Theoretical lenses from the Viable Systems Approach (VSA), Stakeholder Theory and Social Network Analysis are utilised to examine the various aspects of the research problem. A multiple-case-study design has been adopted for exploring the nature of integration and the location of value co-creation leading to a successful market-based ecosystem. An Actor-Classification typology matrix and a Value Co-creation ‘Cloud’ framework are proposed in the conceptual framework and examined during the empirical part of the study. The findings of the study lend initial support for the proposition that increased bonding between the firm-actors and the community-actors can lead to an enhanced value co-creation ‘cloud’ which spreads its benefits over multiple categories of actors in the ecosystem. Besides this, the study also presents a detailed set of factors which appears to strengthen the firm-actor bonds, and explains the process through which these relationships are formed. The study contributes towards an improved understanding of the dynamics that support the development and sustenance of strong and sustainable bonds by the firm with the multiple actor-groups, as well as explains their influence on the value co-generated within the BOP ecosystem.Non
A systematic review of Arabic text classification: areas, applications, and future directions
No full textAbstract
Text classification pertains to the automated procedure of assigning predefined labels or categories to textual data. A
comprehensive review of the existing literature on Arabic text classification (ATC) reveals that most research concentrates
on methodologies and approaches, with no thorough evaluation of ATC. Consequently, this systematic review aims to offer
a comprehensive understanding of the state-of-the-art in ATC, illuminate the present challenges, and discuss prominent
trends in large-scale research. From a collection of 2875 studies, 60 were determined to satisfy the eligibility criteria and
were rigorously analyzed. The selected studies were divided into three categories: topic areas, tasks/applications, and ATC
phases. The topic areas were classified into six primary sectors: healthcare, legal, security and cybersecurity, history,
culture and religion, social media, and agriculture. The ATC tasks/applications were classified into nine groups: gender
identification, author identification, disease detection, threat and spam detection, dialect identification, hierarchical cate gorization, news article classification, web page clustering, and question classification. The ATC phases were organized
into five categories: corpus creation, preprocessing (stemming and tokenization), feature selection, feature extraction, and
classifiers/approaches. The review emphasizes the proposed solutions in each ATC study and offers insights for future
research. This review also underscores the potential applications of ATC in addressing current challenges across various
industries and highlights the significance of developing a benchmark dataset for ATC to facilitate model comparison. The
review concludes by proposing areas where further research is required, such as addressing the unbalanced dataset issue,
enhancing the preprocessing phase, and exploring human factors’ role in utilizing ATC systems
Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
No full textAs computer networks become increasingly important in various domains, the need for
secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled
supply chain networks. One way to ensure network security is by using intrusion detection systems
(IDSs), which are specialised devices that detect anomalies and attacks in the network. However,
these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping,
which can undermine their effectiveness within blockchain-enabled supply chain networks. In this
research paper, we investigate the effect of these attacks on a network intrusion detection system
using several machine learning models, including logistic regression, random forest, SVC, and XGB
Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each
model three times: once without any attack, once with random label flipping with a randomness of
20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally,
this research tests an eight-layer neural network using accuracy metrics and a classification report
library. The primary goal of this research is to provide insights into the effect of data poisoning
attacks on machine learning models within the context of blockchain-enabled supply chain networks.
By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to
the specific challenges of securing blockchain-based supply chain networks
AGENT-BASED SIMULATION FOR UNIVERSITY STUDENTS ADMISSION: MEDICAL COLLEGES IN JORDAN UNIVERSITIES
No full textMedical colleges are considered one of the most competitive schools compared to other university
departments. Most countries adopted the particular application process to ensure maximum fairness
between students. For example, in UK students apply through the UCAS system, and most of USA
universities use either Coalition App or Common App, on the other hand, some universities use their own
websites. In fact, a Unified Admission Application process is adopted in Jordan for allocating the students
to the public universities. However, the universities and colleges in Jordan are evaluating the applicants by
using merely the centralized system without considering the socioeconomics factor, as the high school GPA
is the essential player their selection mechanism. In this paper, the authors will use an Agent Based model
(ABM) to simulate different scenarios by using Netlogo software (v. 6.3). The authors used different
parameters such as the family-income and the high school GPA in order to maximize the utilities of the
fairness and equalities of universities admission. The model is simulated into different scenarios. For
instance, students with low family income and high GPA given them the priority in studying medicine
comparing with same high school GPA and higher family-income, as a results, after several rotations of
the simulation the reputation of medical schools are identified based on students’ preferences and seats’
allocated as it shows that high ranking universities are mainly allocated with have high cut-off GPA score
Big Data Analytics from the Rich Cloud to the Frugal Edge
No full text—Modern systems and applications generate and con sume an enormous amount of data from different sources,
including mobile edge computing and IoT systems. Our ability to
locate and analyze these massive amounts of data will shape the
future, building next-generation Big Data Analytics (BDA) and
artificial intelligence systems in critical domains. Traditionally,
big data materialize in a centralized repository (e.g., the cloud)
for running sophisticated analytics using decent computation.
Nevertheless, many modern applications and critical domains
require low-latency data analysis with the right decision at
the right time standard for building trust. With the advent of
edge computing, that traditional deployment model shifted closer
to the data sources at the network’s edge. Such a shift was
motivated by minimized latency, increased uptime, and enhanced
efficiencies. This paper studies the BDA building blocks, analyzes
the deployment requirements for edge-based BDA QoS, and
drafts future trends. It also discusses critical open issues and
further research directions for the next step of edge-based BDA
Artificial Intelligence Chatbots: A Survey of Classical versus Deep Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques
No full textArtificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which
machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed
to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in
many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots
have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus
create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to
develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed
for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared
to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did
not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and
(b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of
chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for
Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data
from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be
done in the future
I Will Survive: An Event-driven Conformance Checking Approach Over Process Streams
No full textOnline conformance checking deals with finding discrepancies be tween real-life and modeled behavior on data streams. The current
state-of-the-art output of online conformance checking is a prefix alignment, which is used for pinpointing the exact deviations in
terms of the trace and the model while accommodating a trace’s
unknown termination in an online setting. Current methods for
producing prefix-alignments are computationally expensive and
hinder the applicability in real-life settings.
This paper introduces a new approximate algorithm – I Will
Survive (IWS). The algorithm utilizes the trie data structure to
improve the calculation speed, while remaining memory-efficient.
Comparative analysis on real-life and synthetic datasets shows
that the IWS algorithm can achieve an order of magnitude faster
execution time while having a smaller error cost, compared to the
current state of the art. In extreme cases, the IWS finds prefix alignments roughly three orders of magnitude faster than previous
approximate methods. The IWS algorithm includes a discounted
decay time setting for more efficient memory usage and a look ahead limit for improving computation time. Finally, the algorithm
is stress tested for performance using a simulation of high-traffic
event streams
The role of blockchain in enabling inter organisational supply chain alignment for value co-creation in the construction industry
No full textIn the construction industry context, misalignments in the supply chain pose significant chal
lenges, hindering successful project delivery. To address these issues, blockchain technology
emerges as a promising IT-based solution for achieving supply chain alignment. A conceptual
model is developed based on the service-dominant logic theory that explores the impact of
blockchain on supply chain alignment and co-created value outcomes within the Business-to-
Business (B2B) construction context. Through a questionnaire-based approach, data were col
lected from 324 respondents in the global construction industry, which was then analyzed using
descriptive and inferential statistics. The findings demonstrate the positive impact of implement
ing blockchain technology on competency, behavioural, process, and expectations alignment
among supply chain partners. These improvements in alignment collectively contribute to the
realization of supply chain value outcomes. These results emphasize the importance of a com
prehensive approach combining technology with alignment efforts to realize blockchain-enabled
value co-creation in construction supply chain management