Abstract: The government's adoption of cloud computing is critical for digital transformation, but it faces persistent concerns over information security, privacy, governance, and risk. This study examines the factors influencing a government's intention to adopt cloud services, adapting the Unified Theory of Acceptance and Use of Technology (UTAUT) with con-structs tailored to the public sector. A cross-sectional survey was conducted across 90 Ni-gerian government organisations, producing 230 valid responses from IT professionals, administrators, and policy personnel. The statistical analysis of the data was conducted using SPSS and structural equation modelling in AMOS. Validity and reliability were con-firmed through composite reliability, Cronbach’s alpha, and discriminant validity measures. Findings show that privacy (β = 0.11, p < 0.05), governance framework (β = 0.34, p < 0.001), performance expectancy (β = 0.38, p < 0.001), and information security (β = 0.10, p < 0.05) significantly influence government intention to adopt cloud services. Perfor-mance expectancy emerged as the strongest predictor. Contrary to expectations, perceived risk did not significantly moderate the relationships, and interaction terms were non-significant. The final model explained 45% of the variance in adoption intention (R² = 0.45). The study highlights the importance of strengthening governance frameworks, em-phasising tangible performance outcomes, and positioning information security and pri-vacy as an enabler of adoption rather than a barrier. By adapting UTAUT to the govern-ment context and disentangling the role of perceived risk, the study offers both theoretical refinement and practical guidance for policymakers aiming to accelerate digital transfor-mation and secure cloud adoption.

Abstract:

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement.

Abstract: Intelligent voice assistants are now widely used on smartphones and embedded boards, where short response time and stable operation are essential. Heavy computation and limited hardware, however, constrain efficiency. This study tested a dual-path method that applied fine-grained memory control together with asynchronous scheduling. A total of 110 trials were run in both laboratory and office conditions. Results showed that median latency fell by 37.3% and 95th percentile latency by 39.8%. Jitter was reduced by 24.6%, and timeout events dropped by 74% compared with baseline runs. Accuracy remained stable, with word error rate changes not exceeding 0.2 and F1 score changes not exceeding 0.3. The results indicate that combining algorithm-level and system-level methods gives stronger benefits than using them alone. The study also reports jitter and timeout metrics, which are often not considered in related work. These findings suggest that dual-path optimization can support efficient and reliable deployment of voice assistants on edge devices. The main limits are the small number of device types, short test periods, and the use of only English speech. Future work should extend to multilingual datasets, longer trials, and secure execution tests.

Abstract: In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose the Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, leveraging immutable ledgers and smart contracts, the framework ensures tamper proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations, such as alerts or device isolation. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. It analyzes common network vulnerabilities (e.g. open ports, remote access, disabled firewalls), attacks (including spoofing, flooding, DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain enhanced approach streamlines security analysis, extends framework for AI threat detection with improved accuracy, reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution.

Abstract: Provenance-based Intrusion Detection Systems (IDS) model the causal relationships between security events through a provenance graph and learn contextual information to detect Advanced Persistent Threats (APTs) effectively. However, existing provenance graph representation methods fail to fully reflect the characteristics of security domain data and the semantic information embedded in system logs, resulting in limitations in learning efficiency and detection accuracy. This paper proposes a provenance representation method that effectively captures security context from system log data. The proposed method improves the performance of provenance-based IDS by combining (1) a provenance graph construction technique that transforms meaningful string attributes—such as command lines, process names, and file paths—into vector representations to extract semantic information in the security context, (2) a hybrid time-position embedding technique for capturing causal relationships between events, and (3) an iterative refinement learning strategy tailored to the characteristics of system log data. Experimental results using the DARPA Transparent Computing Engagement 3 (E3) benchmark dataset for APT detection demonstrate that our method achieves improved accuracy compared to existing approaches while significantly accelerating convergence during iterative training. These results suggest that the proposed embedding technique can more effectively capture abnormal temporal patterns, such as long dwell times characteristic of APT attacks.

Abstract: With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standard-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. As a result of the study, we infer that the DT framework offers significant benefits in facilitating transparent and seamless cooperation between agents and humans within increasingly autonomous industrial MAS.

Abstract: The contemporary digital landscape overwhelms visitors with fragmented and dynamic information, complicating travel planning and often leading to decision paralysis. This paper presents a real-world case study on the design and deployment of an intelligent tourism assistant for Valencia, Spain, built upon a Retrieval-Augmented Generation (RAG) architecture. To address the complexity of integrating static attraction data, live events, and geospatial context, we implemented a multi-agent system comprising specialized Retrieval, Events, and Geospatial Agents. Powered by a large language model, the system unifies heterogeneous data sources — including official tourism repositories and OpenStreetMap — within a single conversational interface. Our contribution centers on practical insights and engineering lessons from developing RAG in an operational urban tourism environment. We outline data preprocessing strategies such as coreference resolution to improve contextual consistency and reduce hallucinations. System performance is evaluated using Retrieval Augmented Generation Assessment (RAGAS) metrics, yielding quantitative results that assess both retrieval efficiency and generation quality, with the Mistral Small 3.1 model achieving an Answer Relevancy score of 0.897. Overall, this work highlights both the challenges and advantages of using agent-based RAG to manage urban-scale information complexity, providing guidance for developers aiming to build trustworthy, context-aware AI systems for smart destination management.

Abstract: Artificial intelligence (AI) systems often need to follow both ethical and legal rules. Sometimes, these rules can conflict. For example, a healthcare AI may need patient consent (ethical rule), but the law might allow data sharing in emergencies (legal rule). This paper introduces a domain-specific language (DSL) to help represent and solve such conflicts. The DSL uses simple and readable syntax so that people without technical training—like ethicists or legal professionals—can write rules clearly. The DSL is automatically translated into formal Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) rules, which work with existing reasoning tools. We tested this DSL in a case study where a medical AI system had to decide whether to access patient data. The DSL helped define when consent is required and when emergency access is allowed. We compared the DSL with writing rules directly in SWRL. The DSL was easier to use, less error-prone, and just as accurate. It also made it easier to update the rules when policies change. This shows that a DSL can support better teamwork between technical and non-technical people. Beyond simplifying rule logic, the DSL improves the transparency and adaptability of normative reasoning in AI. Future work will focus on extending the language’s capabilities to address more nuanced conflicts and scaling its application across broader deployment scenarios.

Abstract: Large Language Models (LLMs) have shown potential across a wide range of applications, yet their adoption, particularly in enterprise settings, has not kept pace with the general enthusiasm. In this paper, we present a novel application of LLMs to convert unstructured text into a structured data format, specifically the Resource Description Framework (RDF)-the lingua franca of the Semantic Web. We demonstrate how, by leveraging the structure of an existing RDF or OWL graph, we can automate prompts which allow for automated construction of Semantic Knowledge Graphs (SKGs).

Abstract: Temporal reasoning is an important part of the field of time geography. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals. While the identification of specific relations is important, the field of qualitative spatial and temporal reasoning relies on conceptual neighborhood graphs to address relational similarity. This similarity is paramount for generating essential decision support structures, notably reasonable aggregations of concepts into single terms and the determination of nearest neighbor queries. In this paper, conceptual neighborhoods graphs of qualitative topological changes in the form of translation, isotropic scaling, and anisotropic scaling are identified using a simulation protocol. The outputs of this protocol are compared to the extant literature regarding conceptual neighborhood graphs of the Allen interval algebra, demonstrating the theoretical accuracy of the work. This work supports the development of robust spatio-temporal artificial intelligence as well as the future development of spatio-temporal query systems upon the spatio-temporal stack data architecture.

Abstract: Cybersecurity breaches in healthcare often stem from human-factor vulnerabilities such as phishing, social engineering, and policy non-compliance. Despite evolving technical defenses, behavioral risk remains a critical gap. This study uses Protection Motivation Theory (PMT) to examine how healthcare cybersecurity professionals perceive and address these threats. Semi-structured interviews with ten professionals revealed five themes: (1) tension between clinical workflows and security, (2) limited impact of generic training, (3) policy inconsistencies among leadership, (4) value of mentorship and IT presence, and (5) need for behavioral design in policies and technology. Findings suggest healthcare cybersecurity must prioritize human-centered design, participatory policy-making, and adaptive interventions, offering practical insights to bolster cyber resilience.

Abstract: Entity resolution in real-world datasets remains a persistent challenge, particularly in identifying households and detecting co-residence patterns within inconsistent and incomplete data. Recent advances using Large Language Models (LLMs) show promise but continue to struggle with scalability, interpretability, and task complexity when applied as single, monolithic systems. This study introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes household entity resolution into coordinated and specialized agents. The system, implemented using LangGraph, includes four agents: a Direct Agent for name-based matching, an Indirect Agent for transitive linkage, a Household Agent for address-based clustering, and a Household Moves Agent for tracking residential relocations. Each agent employs a task-specific RAG retrieval strategy and a hybrid data cleaning pipeline that integrates rule-based and LLM-powered parsing. Evaluated on synthetic S12PX dataset segments containing 200–300 records with extensive duplicates and data quality issues, the framework achieved 94.3\% accuracy on name variations, complete decision transparency, and a 61\% reduction in API calls compared to single-LLM approaches. These results demonstrate that coordinated agent specialization enhances accuracy, efficiency, and interpretability, establishing a scalable paradigm for entity resolution applicable to census operations, healthcare, and other structured data domains.

Abstract:

The explosive growth of one-way data flows in modern interconnects, now routinely in the \( 10^{10}–10^{12} \) b/s (100 Gb–1 Tb) range, shows no sign of slowing. Yet, while one-way throughput scales, two-way (acknowledged) communication remains fundamentally bounded by the round-trip speed-of-light latency. This contradicts assumptions in many network architectures that model performance and congestion control only in terms of one-way delay. A crucial shift emerges when Ethernet frame lengths exceed the physical length of the underlying link. In this regime, acknowledgments of each frame occur during transmission and incur almost no penalty, enabling a reconceptualization of classical Shannon theory. By reversing the time-oriented Turing tapes at both ends of the link and comparing sent and received bits with hardware comparators on the SERDES interfaces, one-way entropy analysis can be generalized into a two-way Shannon channel. This reframing directly integrates feedback into the definition of information itself. The implications extend beyond communications engineering. At the conceptual level, this analysis resonates with well-known debates in the foundations of physics, especially the interplay of information, entropy, and time symmetry. We propose a new model of temporal structure, termed Alternating Causality, which formalizes time as a reversible bidirectional process, and information as a conserved quantity.

Abstract: Educational drones have become important in research due to their affordability, user-friendly design and control, and potential for beginner learning. However, most rely on one-to-one control through radio-frequency remote controller, which creates challenges in coordinating multiple units since inter-device communication is limited. To address this issue, this work uses two architectural approaches with object-oriented methods for CoDrone EDU models and explores a Drone Control Station system to achieve a mission using onboard capabilities such as the color sensor. The first approach is a centralized architecture, connecting multiple drones to a single station via a USB hub. The second approach is a client-server architecture, enabling coordination among many station instances over TCP/IP. Applying object-oriented principles allows the control of multi-drone missions to be organized into well-defined components. With these principles, it is possible to enhance scalability, enabling flexible integration of behaviors and mission requirements. Through flight tests based on the mission of yellow card detection, the control architectures are being tested and compared, focusing on mission time and inspection coverage. Results show that while centralized control is simple and suitable for basic applications, it lacks scalability. In contrast, client-server control improves scalability, though it requires higher implementation effort and a stable network. The flight tests demonstrated approximately 77% reduction in mission time when employing multiple drones compared to using a single drone. We expect our work to serve as one approach for controlling multiple drones with limited capabilities and encourage the use of educational drones in other domains.

Abstract: Collective intelligence within a quantum-informed cybernetic paradigm presents a trans-formative perspective to examine adaptability and resilience in Internet of Things (IoT) systems. This paper introduces Cogitor5, a 5th order cybernetic system that builds upon the foundational principles of the fourth-order COgITOR framework, a computational system designed for complex adaptive processes. The term COgITOR is etymologically linked to the Latin passive verb cogĭtur, translating to "He is gathered," in contrast to the more commonly recognised active form cogito, meaning "I gather" or "I think," as famously articulated by Descartes. In contrast to conventional binary systems, Cogitor5 functions as a simulation-based complex adaptive system, characterised by a population of nano agents represented by nanoparticles suspended in a colloidal medium. These agents ex-hibit autonomous interactions within the solvent, demonstrating quantum-enabled prop-erties that facilitate advanced self-organisation and coevolutionary dynamics. This intri-cate model captures the complexities of agent interaction, offering a refined representation of their evolving collective intelligence. The study redefines collective intelligence as emergent process intelligence, relevant to the adaptive capacities of both biological and cybernetic systems. By utilising metacybernetic principles in conjunction with theories of complex adaptive systems, this paper investigates how IoT networks, powered by colloi-dal nano agents, can evolve to enhance agency trajectory formation and increase adapta-bility. Cogitor5 serves as an innovative computational framework for addressing the in-herent complexities of IoT, providing clarity in examining self-organisation, self-regulation, self-maintenance, and sustainability, thus elevating system viability. The methodology encompasses the modelling of collective and process intelligence within the scope of Mindset Agency Theory (MAT), an advanced metacybernetic model that allows for evaluable characteristics. Furthermore, this approach integrates theoretical modelling and practical case studies to illustrate agency functionality within these dynamic systems.

Abstract: This study proposes a generative recommendation model based on Pinecone vector retrieval and Retrieval-Augmented Generation (RAG), designed for intelligent financial customer recommendation scenarios. Building upon traditional embedding retrieval and deep recommendation methods, the model incorporates multimodal vectorization strategies and the RAG architecture to achieve efficient recall of high-dimensional features and contextually enhanced generation. Experimental results demonstrate that the model achieves Precision@10, Recall@50, and NDCG@10 scores of 0.177, 0.436, and 0.463 respectively, representing improvements of approximately 12%, 11%, and 10% over BERT4Rec, exhibiting superior accuracy and interpretability.

Abstract: Identity and access management (IAM) systems are entering a difficult transition: recovery flows remain attacker-favored, machine identities rotate at scale, and post-quantum cryptography (PQC) introduces larger artifacts and new latency envelopes. Teams need a repeatable way to quantify fraud-versus-friction trade-offs and rollout safety during crypto-agile migrations—without exposing proprietary scoring models. This paper proposes a public, synthetic benchmark and evaluation harness for IAM recovery, sign-in, and credential rotation under PQC-aware conditions. The benchmark contributes (i) event schemas and a configurable generator with knobs for fraud prevalence, distribution drift, and signal dropout; (ii) a PQC “overlay” that models payload sizes and processing overhead for issuance/verification; (iii) simple baseline policies (static MFA, trivial risk); and (iv) reproducible metrics, including fraud blocked (%), legitimate friction (%), p95 decision latency, time-to-innocence, rotation SLO pass rate, and migration health (%C/%H/%Q). We report baseline results and stress tests and release code and documentation to enable independent replication and extensions. This work is the first step in a broader research agenda on SOMA, a risk-aware orchestrator for recovery and machine identities; system internals remain out of scope here and will be detailed in subsequent publications. (A patent application is pending on SOMA’s underlying mechanisms; the benchmark is designed to remain IP-safe while still supporting rigorous comparison.)

Abstract: Purpose: This paper presents a comprehensive review of Enterprise Architecture (EA) evolution, bridging the gap between academic research and industry practice over the past decade (2015-2025). We examine how EA frameworks, methodologies, and practices have adapted to digital transformation challenges, with particular focus on financial services sector implementations. Design/methodology/approach: We conducted a systematic literature review of 150+ peer-reviewed articles from leading databases (Scopus, Web of Science, IEEE Xplore) combined with industry reports and practitioner insights from enterprise architecture implementations in banking and financial services. The review follows the PRISMA guidelines and includes both theoretical contributions and empirical studies. Findings: The research reveals five major evolutionary trends: (1) shift from rigid frameworks to agile EA practices, (2) integration of EA with DevOps and continuous delivery, (3) emergence of EA automation and AI-assisted tools, (4) increased focus on business outcome measurement, and (5) evolution toward ecosystem and platform-based architectures. We identify a persistent gap between academic frameworks and practical implementation challenges, particularly in legacy system modernization and organizational change management. Practical implications: The paper provides actionable insights for EA practitioners, highlighting successful patterns from banking sector implementations, common pitfalls, and emerging best practices for digital transformation. We present a maturity-based adoption roadmap suitable for organizations at different stages of EA evolution. Originality/value: This is the first comprehensive review that systematically integrates academic research with large-scale industry implementation experiences, offering a bidirectional knowledge transfer between theory and practice. The inclusion of practitioner perspectives from financial services provides unique insights into real-world EA challenges and solutions.

Abstract: The evolution of digital technologies affects parliaments worldwide, triggering investigations to change their operational functions. Being rather conservative organizations, parliaments use dig-ital tools and services that tend to adopt mature emerging technologies (ParlTech) for their digi-tal transformation. Inevitably, digital parliamentary environments become amalgams of several components, features, and types. This research effort is mainly a literature review of the term ‘digital parliament’, and the digital technologies used for the creation of a digital parliamentary environment. It discusses the findings during the last years and identify the research gaps on them. A set of answers is also provided as a roadmap for ‘digital’ parliament creation.

Abstract: Temporal variability in online streams arises in information systems where heterogeneous modalities exhibit varying latencies and delay distributions. Efficient synchronization strategies help to establish a reliable flow and ensure a correct delivery. In this paper, we establish a formal modeling foundation to address temporal dynamics in streams with multimodality using a discrete event system specification framework. This specification captures different latencies and interarrival dynamics inherent in multimodal flows. We also incorporate a Markov variant to account for variations in delay processes, thereby capturing timing uncertainty in a single modality. The proposed models are modular with inherent accounts for diverse temporal integration, thereby facilitating heterogeneity in information flows and communication. Various structural and behavioral forms can be flexibly represented and readily simulated. We demonstrate the experiments of several model permutations through the time-series behavior of individual stream components and the overall composed system, highlighting performance metrics in both, while quantifying the composability and modular effects.