Search Results (3,089)

Recent cyber incidents have become increasingly sophisticated through Living-off-the-Land (LotL) techniques that exploit legitimate behavior and multi-stage attacks. This requires advanced reasoning capabilities to discern the attack contexts within fragmented large-scale logs. However, closed network environments with physical network separation (air-gapped), such as national critical infrastructures, restrict the use of high-performance cloud large language models (LLMs), thereby limiting the adoption of cutting-edge artificial intelligence (AI)-based analysis technologies. To overcome these constraints, this study proposes a Local LLM-based intrusion analysis framework that operates independently within closed networks. The proposed framework combines (i) an Offline Knowledge Distillation technique that transfers the analytical reasoning process of external high-performance models to the Local LLM after a security review, and (ii) an AI agent orchestration structure that controls the analysis procedure step-by-step and suppresses hallucinations. Experiments and validation using a public dataset (Atomic Red Team) demonstrated that the proposed model achieved a consistently higher detection accuracy (88.4%) and MITRE Adversarial Tactics, Techniques, and Common Knowledge mapping performance (0.91 F1-Score) than existing general-purpose Local LLMs. Furthermore, the proposed model suppressed hallucination rates to 6.2% through an automated verification mechanism and significantly improved analysis efficiency by refining large-scale logs to focus on core events. This study quantitatively demonstrated that AI-based intrusion incident analysis can be automated using a single graphics processing unit server under controlled evaluation conditions. The proposed framework provides a practical prototype for intelligent security monitoring in closed-network environments. However, the operational performance must be validated in real-world deployments. Full article

This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence and to advance a physicalist analysis of algorithmic endogenous ethics. Combining a close reading of alignment techniques (RLHF, DPO, iterative DPO, GRPO) with a conceptual analysis grounded in Peter-Paul Verbeek’s theory of technological mediation and moral materialization, the paper traces how value-alignment goals are being “materialized” into internal, dynamic, and evolvable “moral scripts” within the algorithms themselves. The analysis shows that contemporary alignment practices are moving from external ethical discipline toward endogenous norms generated through iterative self-evaluation, asymmetric self-play, and rule-based self-exploration. The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”. Full article

The creation of collaborative knowledge bases for decision support systems (DSS) mitigates the subjectivity of individual experts and enhances overall system efficacy. However, traditional knowledge engineering approaches are highly labor-intensive when eliciting and integrating expert knowledge, as they require extensive, expert-level interaction between knowledge engineers and domain specialists. Modern large language models (LLMs) and retrieval-augmented generation (RAG) technologies present novel opportunities for overcoming these limitations. This study presents a pilot investigation to assess the potential of LLM-based knowledge engineering for developing collaborative knowledge bases within knowledge-based DSS, thereby assisting decision-making in complex operational scenarios involving technical systems. The article proposes an LLM-based approach for creating collaborative knowledge bases, including extraction, consolidation of expert knowledge and evaluation of their operability. To implement and evaluate the proposed approach, specialized prompts were engineered, and pilot experiments were conducted to generate consolidated knowledge cases through expert-LLM interactions. The resulting knowledge cases were subsequently applied in an experimental decision-making inference procedure for fault diagnosis in gas-fired heating boilers. During this inference process, an LLM agent, guided by tailored prompts and a RAG-enabled knowledge base, interactively queries the user to identify the specific issue and subsequently proposes a contextually appropriate solution. Throughout this study, the LLMs demonstrated capabilities in dialogue management, expert knowledge elicitation, and knowledge consolidation, successfully facilitating the creation of a collaborative knowledge base grounded in the “Event-Cause-Symptoms-Action” model. The findings highlight the viability of future research in LLM-based knowledge engineering and support the further advancement of the “LLM-as-knowledge-engineer” paradigm. Full article

In the context of industrial digitalization and the widespread adoption of process automation systems, Knowledge Management Systems (KMS) play a key role in providing operational personnel with up-to-date instructions and regulations. However, the inherent ambiguity of natural language in technical documentation remains a serious obstacle, leading to incorrect operator actions, process deviations, and increased safety risks. This article investigates the integration of Large Language Models (LLMs) into KMS and its impact on user experience and human–machine interaction in industrial automation environments. A method called Semantic Latent Choice Detection is presented, designed to systematically identify interpretation ambiguities in process instructions and operator commands. Unlike existing approaches that require access to the internal model architecture (“white box”) or token-level logits, the proposed method is logit-free and operates with closed commercial LLMs (“black box”) via standard API interfaces. The method analyzes the semantic similarity of binary text blocks and polysemous terms within the context of a specific technological process. Using a metallurgical production case study, we demonstrate how the system detects hidden semantic collisions (e.g., the difference between “adding ferroalloys into the ladle” and “feeding ferroalloys onto the conveyor”) that are missed by traditional rule-based validation methods. Instead of arbitrarily selecting an interpretation, the system initiates a clarification request to the human operator, thereby reducing cognitive load, preventing erroneous automated decisions, and increasing trust in the KMS. An empirical evaluation conducted in a real-world industrial setting (unit control rooms and dispatch centers) shows a statistically significant reduction in errors related to misinterpretation of process regulations. The article contributes to the fields of automation engineering, knowledge management, and human-centered automation by proposing a novel method for validating operational instructions in high-risk industrial environments. Full article

The unprecedented rate of technological advances, accelerated industry disruptions and social and environmental sustainability crises require very different business organizations from the traditional paradigm. The main research question for this paper is: What change (paradigm shift) is needed for South African business organizations to be future-fit? The paper introduces an integrated, transdisciplinary paradigmatic model of an emerging, progressive future business organization in South Africa, as mostly influenced by Western futurists, and proposes an understanding of the paradigm shift required in our socially constructed reality for such organizations to emerge. A multi-method methodology, based on complexity theory and a transdisciplinary approach, was developed and applied. The researcher’s conceptualization of a ‘paradigm’, focusing on language-based representations, is explicated as a theoretical foundation. Textual analyses, including corpus linguistics, of practitioner-focused literature were used to elicit concept maps (or domain models) of the shared, societal-level mental models of a South African business organization for two periods: (1) the Traditional Business Organization, and (2) a Progressive Future Business Organization. The outcomes were compared using a novel qualitative method, resulting in a proposed set of societal-level ontological shifts needed for a progressive organizational future. The study shows a paradigm shift to complexity and social responsibility, and the need for transdisciplinarity to reflect complex, integrated organizational realities. Full article

Artificial intelligence is rapidly changing education. However, many learning chatbots are still reactive tools, which respond to arbitrary questions without leading learners through a meaningful pedagogical journey. This article presents a deterministic state-machine orchestrator coupled with a local large language model and a knowledge-graph-framed tutoring strategy for personalized education. The proposed virtual tutoring agent is designed to combine the flexibility of conversational AI with the reliability of explicit instructional states, key performance indicator (KPI) tracking, learner profiling, and controlled transitions between explanation, practice, feedback, assessment, and remediation. The system is not meant to replace the teacher, but rather to act as a teaching co-pilot that provides ongoing feedback, personalized learning paths, accessibility, and safer deployment by processing data locally. The study also presents a compact interview-based evaluation framework and statistical analysis of user perceptions across interactivity, individuality, proactivity, security, accessibility, gamification, and global preference for educational agents over classical chatbots. The findings show that learners appreciate personalized and interactive support and that proactivity is the key feature that distinguishes an educational agent from a regular chatbot. With this article we argue that deterministic orchestration can help make AI tutoring more transparent, controllable, and ethically fit for real learning contexts. Finally, it discusses privacy, educational value, limitations and future improvements to be made before the large-scale adoption of such systems. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article

Information technology service management (ITSM) systems generate large volumes of unstructured incident descriptions. They frequently include multilingual content, code-switching, informal language, and domain-specific terminology. These characteristics make automated text processing substantially more complicated and limit the applicability of conventional machine translation solutions, particularly in environments subject to strict data privacy and air-gap constraints. This paper presents a system-level reproducibility study of a deterministic two-stage cascaded neural machine translation (NMT) pipeline for normalizing multilingual IT incident text in resource-constrained, air-gapped environments. The study evaluates a sequential RU→EN and LT→EN translation strategy specifically selected to bypass unreliable language identification, enabling stable processing of code-switched incident descriptions. A system-level processing pipeline, which includes text normalization, segmentation, deduplication, adaptive batching, and language-aware data flow optimization, is analyzed to assess its impact on reducing redundant inference operations. The methodology is evaluated on a real-world ITSM dataset comprising 84,285 incident records. An incremental experimental design is used to isolate the specific contributions of computational and data-flow optimizations. Translation quality is assessed using BLEU and COMET metrics against expert reference translations produced via a primary translation and subsequent cross-verification by a second domain expert to ensure linguistic and technical consistency. The results indicate that a cascaded NMT architecture combined with systematic data-flow optimization provides a reproducible and privacy-preserving framework for multilingual IT incident text normalization, effectively supporting downstream analytical tasks in constrained operational ITSM environments. Full article

This study investigated how children aged 7 to 11 years process and comprehend written irony with different emotional valences (ironic praise vs. ironic criticism) using eye-tracking technology. Participants read short stories containing literal praise, literal criticism, ironic praise, or ironic criticism while their eye movements were recorded. Results indicated that children showed significantly lower comprehension accuracy for ironic praise compared to ironic criticism, supporting the affective asymmetry hypothesis in irony processing. Eye-tracking data provided partial support for this asymmetry: regression-path durations—but not first-pass or total reading times—were longer for ironic utterances, particularly ironic praise, indicating greater effort during integrative rereading and reanalysis. Age-related differences were limited to regression-path duration rather than comprehension accuracy, suggesting selective developmental differences in online integration. These findings provide process-level evidence for children’s written irony comprehension and highlight the role of online integrative processes in figurative language processing. Full article

Next-generation communication technologies are increasingly shaping not only network infrastructure and digital services, but also public expectations, risk perceptions, and policy debates. As 5G deployment continues and 6G research accelerates, social responses to communication technologies have arguably become an important dimension of technology adoption, governance, and regulatory decision-making. Social media platforms provide timely and large-scale data sources for public opinion analysis. However, 5G/6G-related discourse often contains domain-specific terminology, technical complaints, and complex emotional expressions, which pose challenges for sentiment analysis. To address this challenge, this study constructs a manually annotated dataset of 1746 5G/6G-related Twitter posts collected across multiple communication-related events. This study aims to provide a domain-specific empirical evaluation of sentiment analysis models by examining classification performance, deployment-oriented inference efficiency, and lightweight domain adaptation. Three sentiment analysis methods are evaluated: twitter–roberta–base–sentiment, bertweet–base–sentiment–analysis, and VADER. In addition, a filtered Amazon Reviews’23 subset is used as an external review-style dataset, and a LoRA-based fine-tuning experiment is performed on Twitter-RoBERTa to examine domain adaptability. The results show that pre-trained language models achieve stronger classification performance than the rule-based method, particularly for domain-specific and semantically complex texts. VADER, by contrast, shows high observed efficiency under its CPU-based deployment setting, especially for short-text inference. The LoRA fine-tuned RoBERTa model further improves classification performance on both Twitter and Amazon test sets, indicating that lightweight parameter-efficient adaptation can enhance model robustness in specialized 5G/6G discourse. These findings contribute a domain-specific dataset, a deployment-oriented comparison of sentiment analysis paradigms, and empirical evidence on lightweight domain adaptation for 5G/6G-related public opinion monitoring. Full article

This systematic review paper examines how technical, operational, and structural factors influence the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa. Guided by PRISMA protocols, the study applies a hybrid thematic synthesis across six dimensions: technical, operational, policy and regulatory, governance, social and cultural, and environmental. The findings indicate that digital tools can generate substantial benefits, including yield increases of 10–30% (documented primarily for mobile-based advisory services and precision input management in East African horticulture and West African cocoa value chains) and price gains of 15–25%, with adoption rates of 70–80% in settings characterised by robust infrastructure, strong institutional support, and effective value chain integration. However, these benefits are unevenly distributed and tend to concentrate in “islands of adoption” characterized by robust infrastructure, strong institutional support, and effective value chain integration. While technical (94.9%) and operational (91.5%) factors dominate the literature, their impact is constrained by persistent structural barriers, including weak policy implementation (79.7%), fragmented governance systems (76.3%), and socio-cultural exclusion—such as gender disparities, age-related digital divides, and language misalignment (71.2%). The review identifies five minimum conditions for meaningful adoption: (i) affordable connectivity and access to digital devices; (ii) context-specific digital literacy; (iii) culturally relevant, user-centred design; (iv) robust institutional ecosystems; and (v) enabling policy and financial frameworks. Overall, the findings underscore that digital agriculture adoption is a socio-technical process shaped not only by technological innovation but also by institutional arrangements and user capabilities. Comparative cases, such as Kenya’s Farm.ink and the less successful EZ Farm initiative, further highlight the importance of integrated, context-responsive approaches to ensure that digital agriculture enhances, rather than marginalizes, small-scale farmers. Full article

Paprika is a spice obtained from the dehydration and grinding of red pepper fruits, primarily from the Capsicum annuum species. Its etymology comes from Slavic Balkanian languages and was adopted in Hungarian. The crop originated in America, where it was domesticated by pre-Columbian civilizations over 6000 years ago (specifically in present-day Mexico) for medicinal and culinary purposes. Following the Spanish arrival in the Americas in the 15th century, pepper was introduced first in Spain (Sevilla, Extremadura and Murcia) and later in the rest of the Old World. The agroclimatic conditions of different Mediterranean regions made it an essential crop, turning these regions into centers of production and giving this spice a sense of cultural identity. The purpose of this study lies in the technological and nutritional significance of paprika in the modern food industry, where it is demanded as a natural colorant, preservative and source of bioactive compounds, such as antioxidants and carotenoids. Despite its prevalence, the existing literature is often fragmented into specific disciplines. This article distinguishes itself by proposing a holistic approach expanding the study from its historical evolution to its socioeconomic impact, including its agronomic characteristics and industrial-scale production. It is recommended that the research community and producers focus on the sustainability of processing methods while preserving cultural authenticity, ensuring the preservation of the functional and culinary relevance of this spice. Full article

Despite the recent surge in research on the use of AI in English language learning, little attention has been paid to its role in improving phonological awareness among preschoolers. Most existing studies have focused on general literacy skills or older learners, with insufficient emphasis on early phonemic awareness and its subskills. Furthermore, there is a lack of research examining these relationships within Arab or multilingual contexts. This study investigates the impact of artificial intelligence (AI) tools on the development of English phonological awareness in kindergarten children in an Arab educational context in Jordan using a quasi-experimental design. The participants comprised 45 students divided into two groups: a control group (n = 23), consisting of 14 females and 9 males, and an experimental group (n = 22), consisting of 12 females and 10 males. All participants were physically and mentally healthy 5–6 year-old children from similar socioeconomic and cultural backgrounds. The experimental group was taught via the AI-based Starfall platform and the control group was taught via conventional teacher-oriented instruction. Both groups were given pre- and posttests, which included assessments of five phonemic awareness skills: initial sound recognition, blending, segmentation, deletion, and substitution. Descriptive statistics, including means and standard deviations, and independent-samples t-tests were calculated to determine the effect of the AI program on developing kindergarteners’ phonemic awareness compared with conventional teaching methods. The findings of the study show significant improvements in the experimental group compared with the control group. Bringing AI into the kindergarten classroom may improve literacy instruction and, in turn, early reading readiness through engaging, interactive and adaptive learning experiences. Full article

The evolution of smart educational platforms has been significantly driven by the global development of ML (Machine Learning), particularly through the application of NLP (Natural Language Processing) to personalize student learning experiences. However, high schools in Gauteng Province face significant challenges in adopting smart educational portals. This is due to inadequate accessibility to resources, insufficient personalization mechanisms, and low student engagement frameworks caused by the failure to adapt to proven teaching methods. Despite the successful adoption of NLP and ML in global case studies and implementations, local gaps persist. Specifically, pretrained LLMs (Large Language Models) such as BERT (Bidirectional Encoder Representations from Transformers) require fine-tuning for African languages, yet little testing of these tools in Gauteng’s public high schools has been done. This review uses a structured literature review methodology, which examines peer-reviewed studies, case reports, and technical documents published between 2014–2026. Findings indicate that multilingual NLP resources for South African languages remain severely underdeveloped. Furthermore, it demonstrates that current smart-learning portals lack inclusive design adjustments for multilingual and low-resource contexts. Based on these findings, the paper recommends strategies for enhancing accessibility, personalization, and engagement. This includes the development of multilingual NLP resources, optimization of ML architectures for constrained infrastructure, and context-aware pedagogical adaptations. The review follows a two-layer design, consisting of (i) a global systematic synthesis of NLP and ML applications in education, and (ii) a contextual interpretation of these findings for Gauteng high schools using regional policy documents, infrastructure reports, and educational statistics. The conclusions pertain to implications and recommendations for Gauteng high schools, rather than evaluation of an existing local portal. This paper highlights the potential of NLP and ML to transform education in Gauteng but highlights the urgency of localized research and ML implementation. Full article

The circular economy is recognised as a key topic that requires the development of user-friendly methodologies for circularity assessment, with digitalisation supporting more accurate evaluation processes. This study proposes an automated digital tool to calculate products’ Circularity Level (LC), defined in UNI/TS 11820:2024 and aligned with ISO/TC 323 circular economy standards (ISO 59004, ISO 59010, and ISO 59020) and the Level(s) EU sustainability framework. Specifically, an Excel-based calculator is developed to encode regulatory requirements, automatically compute LC values, generate radar charts highlighting improvement areas, and export results to MS Word for automated stakeholder reporting. Additionally, for construction materials, an information flow between MS Excel and Autodesk Revit is established using Dynamo, enabling the automated creation of product-related BIM objects and the integration of circularity data into the BIM model. The workflow is demonstrated through its application to a single case, namely the ITER Project, which implements earthen plasters enhanced by by-products from the agricultural and stone supply chains. An LC of 43.77% is obtained, driven by material efficiency and recovery, but limited by renewable energy use and end-of-life management. Future research will investigate AI techniques to optimise indicator scores and enhance digital circularity assessment in the construction sector. Full article