Nur Siyam

Integrity enforcement in higher education increasingly rests on detectors of machine-written text, yet these detectors are defeated by paraphrase and trivial edits and disproportionately misclassify students who write in English as an additional language. This article proposes SCRIBE, a framework in which the reportable offence is a declaration that misdescribes how the work was produced. Students declare an assistance level and attest to their own authorship, while an instrumented editor commits process digests to a permissioned ledger. Seven smart contracts bind these commitments to the governing policy and encode a due-process procedure that no software agent can complete; four agents prepare each case, a human panel decides it, and abstention is always available. We evaluate the framework as a mechanism-verification exercise on 60,000 synthetic submissions whose behaviour classes, generator parameters and oral-probe response rates are stated assumptions, so the figures below describe that specified model rather than expected real-world performance. Within a false accusation budget of 0.5%, SCRIBE refers 30.6% of misdeclarations at 97.1% precision, wrongly referring 73 of 22,131 truthful submissions, whereas a simulated detector refers 0.3% at the same budget. Recall ranges from 5.0% to 52.3% across plausible oral-probe parameters. Process evidence alone cannot exceed a posterior of 0.604, and a replayed writing trace cuts referral to 7.2%. For a single institution, a witnessed append-only log provides four of the six procedural guarantees; the ledger’s distinctive value lies in multi-institution settings where no party is trusted to operate the log.

Blockchain technology has increasingly drawn the attention of governments seeking to modernize public services through transparent, secure, and efficient digital infrastructures. Drawing on case studies from diverse regions, including the UAE, Estonia, Georgia, Colombia, and multiple Gulf Cooperation Council (GCC) nations, this systematic review synthesizes implementation patterns across domains such as land administration, digital identity, procurement, and intergovernmental payments. The critical analysis highlights blockchain’s capacity to establish tamper-evident records, automate verification, and reduce administrative overhead while also addressing technical and institutional factors that shape its impact. Outcomes across successful deployments suggest that benefits are most pronounced when blockchain aligns with real governance needs and is supported by robust legal and digital infrastructure. This review also identifies key barriers to adoption, including interoperability challenges, regulatory uncertainty, limited technical capacity, and resistance to organizational change. Notably, this review highlights a critical but underexplored dimension involving the need for public accountability not only in service delivery but also in the governance of blockchain systems themselves. By examining real-world use cases alongside technical and policy frameworks, this review advances a deeper understanding of blockchain’s role in reshaping public administration and sets a research agenda for building more trusted, auditable, and inclusive digital government systems.

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

Customs duty obligations are primary revenue sources for nations. Any under or over value can result in economic instability. The role of customs is safeguarding society and local businesses from criminally guided value manipulations. In this paper, a cluster-based approach to detect the customs commodity value manipulation is proposed. This approach represents the shipment related information into 3-dimensional space to determine any wrongdoing through two stages comprising distance- and density-based techniques. This representation simplifies the process by identifying relationships among the various shipment features. These two stages work together in order to analyse the shipments information and determine any abnormal behaviour for these shipments. The results of the proposed approach achieved an accuracy of 86%. This technique will provide much needed capabilities to protect the revenue and securing the trade supply chain from illegitimate activities.

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to solve the problem of selecting the right motivator for children with ASD using Reinforcement Learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov Decision Process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on Applied Behavior Analysis as well as learners’ individual preferences. We use a Q-Learning algorithm to solve the modelled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.