Salman Ahmed

Context: The blockchain technology has become a secure, transparent, and decentralized infrastructure in data handling and its potential in the education sector has gained more and more attention especially in securing academic records, preventing credential fraud, and enhancing confidence in the verification procedures. Nonetheless, regardless of this interest, there is a lack of synthesis of the way in which blockchain is being implemented into real educational environment and the challenge that hinder its use. Objectives: The objective of this study is to conduct a systematic review of applications of blockchain in education, determine the main areas of its implementation, evaluate the advantages and limitations mentioned in the literature, and offer recommendations to subsequent research and real‐world implementation. Methods: The systematic literature review was carried out according to the methodology suggested by Okoli and Schabram. A total of 64 peer‐reviewed studies published between 2017 and 2025 were analyzed. Results: The results showed that blockchain is mostly utilized in credential and certificate management, microcredential and credit transfer, competency and assessment tracking, and secure collaborative e‐learning, with other applications in copyright protection and decentralized examination systems. Compared with conventional educational technologies, blockchain enables tamper‐evident records, independently verifiable credentials, and reduced verification costs through cryptographic auditability and decentralized trust. Contributions: This review provides a consolidated evidence base on blockchain‐enabled education, highlighting realistic implementation opportunities, such as QR‐hash diplomas and inter‐institution credit exchanges, while emphasizing the need for privacy‐preserving architectures and shared governance standards. The findings support researchers, practitioners, and policymakers to assess and promote safe, reliable, and effective blockchain‐based learning ecosystems.

Advances in the healthcare sector need to secure efficient and decentralized systems to handle the large number of images and test reports. Traditional cloud-based healthcare systems pose crucial challenges, such as higher maintenance costs, significant storage demand, and, most importantly, privacy concerns. This chapter introduced the comprehensive patient-centric system for decentralized storage, secure medical data sharing, integrating blockchain technology and distributed file systems. The healthcare system leverages the IPFS and Ethereum blockchain to empowering scalable privacy protection of globally available storage of medical metadata. The framework enhances the accessibility and usability of incorporating a mobile offering for the patients, hospitals, and other stakeholders to interact with the decentralized system through the blockchain-integrated mobile application.This chapter provides an innovative approach to addressing the healthcare centralized data management limitations while ensuring patient privacy and data security.

Metaverse—a three‐dimensional computational environment—combines physical and virtual reality to enable social relationships and immersive experiences by mimicking real‐world scenarios. Metaverse is considered the third wave of the internet revolution (exploiting Web 3.0), leveraging upcoming technologies such as extended reality and artificial intelligence shaping a new era of human–‐machine interactions. In recent years, increased research and development on educational technologies (EduTech) based on blockchain technology has seen substantial growth of metaverse‐based solutions within the higher education context. This research aims to synergize blockchain technology and metaverse environments to conduct online exams (metaExam) in a trustworthy, reliable, and secure way. The synergy between blockchain and the metaverse brings various benefits, such as improved security, cost effectiveness, and increased efficiency in the online examination process. One of the central features of the proposed solution metaExam is to leverage cryptographic protocols via blockchain to control data access, making verification faster and protecting against misuse. Exam scores and grades are stored on a blockchain ledger using a digital signature method to enhance security. We validated the proposed solution by testing a prototype on the Ethereum platform using the Sepolia Testnet network using Microsoft Windows environment. Evaluation results indicate (i) query response time (10–50 ms), (ii) and query execution performance (CPU utilization between 1%–5%) offering computationally feasible solution. This research contributes by integrating blockchain and metaverse technologies to offer a solution metaExam that can offer improved security and immersive user experience for exam management. The proposed solution and its validation can provide insights into transforming online exams, offering a fresh perspective on addressing concerns about exam grade authenticity and verifying academic credentials in EduTech.

The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers are aware of. In our study, we focused on dynamic application security testing (DAST) to investigate cross-site scripting (XSS) attacks. We closely examined 23 popular WordPress plugins, especially those requiring user or admin interactions, as these are frequent targets for XSS attacks. Our testing uncovered previously unknown zero-day vulnerabilities in three of these plugins. Through controlled environment testing, we accurately identified and thoroughly analyzed these XSS vulnerabilities, revealing their mechanisms, potential impacts, and the conditions under which they could be exploited. One of the most concerning findings was the potential for admin-side attacks, which could lead to multi-site insider threats. Specifically, we found vulnerabilities that allow for the insertion of malicious scripts, creating backdoors that unauthorized users can exploit. We demonstrated the severity of these vulnerabilities by employing a keylogger-based attack vector capable of silently capturing and extracting user data from the compromised plugins. Additionally, we tested a zero-click download strategy, allowing malware to be delivered without any user interaction, further highlighting the risks posed by these vulnerabilities. The National Institute of Standards and Technology (NIST) recognized these vulnerabilities and assigned them CVE numbers: CVE-2023-5119 for the Forminator plugin, CVE-2023-5228 for user registration and contact form issues, and CVE-2023-5955 for another critical plugin flaw. Our study emphasizes the critical importance of proactive security measures, such as rigorous input validation, regular security testing, and timely updates, to mitigate the risks posed by zero-day vulnerabilities. It also highlights the need for developers and administrators to stay vigilant and adopt strong security practices to defend against evolving threats.

Decoding Part of Speech(POS) tagging directly from electroencephalography (EEG) signals whilst user overtly spoke (voiced speech) sentences could improve direct speech brain-computer interfaces (BCIs) using imagined or inner speech. To the best of our knowledge, earlier work uses machine learning approach using 74,953 sentences/tokens recorded in 75 EEG sessions. The tokens can be found in 4,479 phrases consisting of terms from the English Online treebank which contains the record of weblogs, newsgroups, reviews, and Yahoo Answers. The results demonstrated the feasibility of POS decoding from EEG based on word class, word frequency, and word length with accuracy of 71%, 86%, 89%, respectively. We believe that there is significant room for improvement with more advanced artificial intelligence. In this paper, we further extend the existing work with end-to-end transformers. Our results presents transformer model outperforms benchmark traditional ML results with +20% in length, +13% for the open vs closed class and +12% in frequency. In our empirical analysis, we find the decoding performance was better when using multi-electrode recordings as compared to single-electrode recordings.

Large-scale companies across various sectors maintain substantial IT infrastructure to support their operations and provide quality services for their customers and employees. These IT operations are managed by teams who deal directly with incident reports (i.e., those generated automatically through autonomous systems or human operators). (1) Background: Early identification of major incidents can provide a significant advantage for reducing the disruption to normal business operations, especially for preventing catastrophic disruptions, such as a complete system shutdown. (2) Methods: This study conducted an empirical analysis of eleven (11) state-of-the-art models to predict the severity of these incidents using an industry-led use-case composed of 500,000 records collected over one year. (3) Results: The datasets were generated from three stakeholders (i.e., agency, customer, and employee). Separately, the bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pre-training approach (RoBERTa), the enhanced representation through knowledge integration (ERNIE 2.0), and the extreme gradient boosting (XGBoost) methods performed the best for the agency records (93% AUC), while the convolutional neural network (CNN) was the best model for the rest (employee records at 95% AUC and customer records at 74% AUC, respectively). The average prediction horizon was approximately 150 min, which was significant for real-time deployment. (4) Conclusions: The study provided a comprehensive analysis that supported the deployment of artificial intelligence for IT operations (AIOps), specifically for incident management within large-scale organizations.

Human error is one of the primary reasons for accidents in complex industries like aviation, nuclear power plant management, and health care. Physical and cognitive workload, flawed information processing, and poor decision making are some of the reasons that make humans vulnerable to error and lead to failures and accidents. In many accidents and failures, oftentimes, vulnerabilities that are embedded in the system, in the form of design deficiencies and poor human factors, lead to latent or catastrophic failures, but the last link is a human operator who gets blamed or worse, injured. This paper introduces an early design human performance assessment framework to identify what type of digital prototyping methodologies are appropriate to detect the deviation of the operator's performance due to an emergency condition. Fire in a civilian aircraft cockpit was introduced as a performance shaping factor (PSF). Ergonomics performance was evaluated using two prototyping strategies: (1) a computational prototyping framework includes digital human modeling (DHM) and computer-aided design; and (2) a novel mixed prototyping framework includes motion capture, DHM, and virtual reality. Results showed that the mixed prototyping framework can simulate emergency scenarios with increased realism and also has the potential to incorporate subjective aspects of ergonomics outcomes, overcoming the underlying lack of design knowledge in conventional early design methodologies.

Since the last decade, Agile software development has emerged as a widely utilized software development method keeping in view the developing countries of South Asia. The literature reports significant challenges and barriers for agile in software industry and thus the area still has significant problems when considered with this domain. This study reports an industrial survey in Pakistani software industry practices and practitioners to elicit the indigenous motivator and demotivators of agile paradigm in Pakistan. This study provides a concrete ranking of motivator and demotivator factors which influence the agile paradigm. A lack of proper training and other identified issues indicate that the adoption of agile is in preliminary phases and serious effort is required to set the direction right for success of agile paradigm and its adopting institutions. The survey is conducted in 23 companies practicing agile organizations and involves 90 agile practitioners. Reports of 67 practitioners were finally selected after careful selection against selection criteria for this study. The results indicate various alarming factors which are different from reported literature on the subject. Tolerance to work is the most important motivating factor among Pakistan agile practitioners, likewise lack of resources is the highest demotivating factor. A detailed ranking list of motivators and demotivators and comprehensive data analysis has been provided in this paper which influences strongly the agile software development issues in Pakistan.