Search Results (1,695)

The academic encounter between Jews and Arabs in Israel carries tensions stemming from a prolonged historical conflict, yet at the same time offers opportunities for authentic engagement that deepens mutual understanding between the groups. This study is grounded in contact theory and multiculturalism, focusing on the integration process of Arab women teachers in a Hebrew-speaking track at an academic college of education. The research explores the participants’ experiences against the backdrop of national tensions, asking how they perceive their teacher education journey in the Hebrew-speaking track in terms of challenges and benefits. The study is based on a qualitative–phenomenological approach, collecting data through interviews with 12 graduates who shared their experiences and reflections. The analysis reveals the participants’ explicit and implicit attitudes, the barriers they faced, and the gains they reported during their studies. Full article

Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to withstand rugged, high-stress environments, and reviews biometric modalities like ECG, PPG, EEG, gait, and voice for continuous or on-demand identity confirmation. Accuracy, latency, energy efficiency, and tolerance to motion artifacts, environmental extremes, and physiological variability are critical performance drivers. Security threats, such as spoofing and data tapping, and techniques for template protection, liveness assurance, and protected on-device processing also come under review. Emerging trends in low-power edge AI, multimodal integration, adaptive learning from field experience, and privacy-preserving analytics in terms of defense readiness, and ongoing challenges, such as gear interoperability, long-term stability of templates, and common stress-testing protocols, are assessed. In conclusion, an R&D plan to lead the development of rugged, trustworthy, and operationally validated wearable authentication systems for the current and future militaries is proposed. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on generative AI in creative domains through a text-mining analysis of nearly 4000 Reddit posts and comments. Drawing on six relevant subreddits from 2022 to 2025, the research investigates the structure of user engagement, interaction dynamics, and language patterns. It identifies dominant terms and phrases related to AI creativity, explores thematic clusters, and compares discussion styles across key tools such as Midjourney, ChatGPT, Stable Diffusion, and DALL·E. Additionally, it provides a sentiment overview based on automated classification and narrative interpretation. The findings show that Reddit users engage with generative AI not only as a set of technical tools but as a source of cultural, ethical, and creative negotiation. This study contributes to a deeper understanding of how digital transformation in creative industries is shaped by public perception, platform discourse, and evolving community norms. Full article

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management. Full article

We present a combined ML/NLP (Machine Learning, Natural Language Processing) pipeline for protecting cloud-based APIs (Application Programming Interfaces), which works both at the level of individual HTTP (Hypertext Transfer Protocol) requests and at the access log file reading mode, linking explicitly technical anomalies with business risks. The system processes each event/access log through parallel numerical and textual branches: a set of anomaly detectors trained on traffic engineering characteristics and a hybrid NLP stack that combines rules, TF-IDF (Term Frequency-Inverse Document Frequency), and character-level models trained on enriched security datasets. Their results are integrated using a risk-aware policy that takes into account endpoint type, data sensitivity, exposure, and authentication status, and creates a discrete risk level with human-readable explanations and recommended SOC (Security Operations Center) actions. We implement this design as a containerized microservice pipeline (input, preprocessing, ML, NLP, merging, alerting, and retraining services), orchestrated using Docker Compose and instrumented using OpenSearch Dashboards. Experiments with OWASP-like (Open Worldwide Application Security Project) attack scenarios show a high detection rate for injections, SSRF (Server-Side Request Forgery), Data Exposure, and Business Logic Abuse, while the processing time for each request remains within real-time limits even in sequential testing mode. Thus, the pipeline bridges the gap between ML/NLP research for security and practical API protection channels that can evolve over time through feedback and retraining. Full article

The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed several challenges, contributions remain fragmented and difficult for non-technical users to apply. This work addresses the following research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems. Full article

This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources—coriander, orange blossom, astragalus, rosemary, and chehelgiah. While HS-SPME-GC-MS combined with traditional chemometrics (e.g., PCA, LDA, OPLS-DA) is well-established for honey discrimination, the application and direct comparison of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models represent a significant advancement in multiclass prediction accuracy and model robustness. A total of 57 honey samples were analyzed to generate detailed volatile organic compound (VOC) profiles. Key chemotaxonomic markers were identified: anethole in coriander and chehelgiah, thymoquinone in astragalus, p-menth-8-en-1-ol in orange blossom, and dill ester (3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran) in rosemary. Principal component analysis (PCA) revealed clear separation across botanical classes (PC1: 49.8%; PC2: 22.6%). Three classification models—RF, XGBoost, and NN—were trained on standardized, stratified data. The NN model achieved the highest accuracy (90.32%), followed by XGBoost (86.69%) and RF (83.47%), with superior per-class F1-scores and near-perfect specificity (>0.95). Confusion matrices confirmed minimal misclassification, particularly in the NN model. This work establishes HS-SPME-GC-MS coupled with deep learning as a rapid, sensitive, and reliable tool for multiclass honey botanical authentication, offering strong potential for real-time quality control, fraud detection, and premium market certification. Full article

Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modalities, as semantic gaps and contextual variations between images and text complicate alignment, interpretation, and the detection of subtle or blatant inconsistencies. To enhance accuracy in fake news detection, this article introduces an ensemble-based framework that integrates textual and visual data using ViLBERT’s two-stream architecture, incorporates VADER sentiment analysis to detect emotional language, and uses Image–Text Contextual Similarity to identify mismatches between visual and textual elements. These features are processed through the Bi-GRU classifier, Transformer-XL, DistilBERT, and XLNet, combined via a stacked ensemble method with soft voting, culminating in a T5 metaclassifier that predicts the outcome for robustness. Results on the Fakeddit and Weibo benchmarking datasets show that our method outperforms state-of-the-art models, achieving up to 96% and 94% accuracy in fake news detection, respectively. This study highlights the necessity for advanced multimodal fake news detection systems to address the increasing complexity of misinformation and offers a promising solution. Full article

Food fraud represents a growing global challenge with significant implications for public health, market integrity, sustainability, and consumer trust. Beyond economic losses, fraudulent practices undermine the environmental and social sustainability of food systems by distorting markets, misusing natural resources, and weakening incentives for authentic and responsible production. Despite the establishment of harmonized frameworks of the European Union for official controls, the increasing complexity of food supply chains has exposed persistent gaps in fraud detection, particularly for high-value products such as those with PDO (Protected Designation of Origin) and PGI (Protected Geographical Ιndication) Certification. This study investigates the perceptions, attitudes, and experiences of frontline inspectors in Greece to assess current challenges and opportunities for strengthening official food fraud controls. Data were collected through a structured questionnaire, validated by experts and administered nationwide, involving 122 participants representing all major national food inspection authorities. Statistical analysis revealed significant institutional differences in perceptions of fraud prevalence, with mislabeling of origin, misleading organic claims, ingredient substitution, and documentation irregularities identified as the most common fraudulent practices. Olive oil, honey, meat, and dairy emerged as the most vulnerable product categories. Inspectors reported relying primarily on consumer complaints and institutional databases as key tools for identifying fraud risks. Food fraud was perceived to contribute strongly to losses in consumer trust in food safety and product authenticity, as well as to the erosion of sustainable production models that depend on transparency, fair competition, and responsible resource use. Overall, the findings highlight detection gaps, uneven resources across authorities, and the need for improved coordination and capacity-building to support more efficient, transparent, and sustainability-oriented food fraud control in Greece. Full article

Teachers’ language assessment literacy (LAL) encompasses the knowledge and competencies required to design and implement assessment practices that support learning. Although prior research has documented general trends in LAL development, less is known about how individual teachers, particularly student teachers, interpret, appropriate, and negotiate formative assessment (FA) task design within the context of initial teacher education (ITE). Adopting an in-depth qualitative case study approach, this study examines how a single student teacher in a Chinese initial teacher education developed her cognition and classroom practice related to FA tasks across a teaching methodology course and a practicum. Drawing on thematic analysis of semi-structure interviews, lesson plans, classroom observations, stimulated recall interviews, and reflective journals, the study traces developmental changes and the contextual factors shaping the student teacher’s LAL in relation to FA tasks. Findings show that the sustained engagement with FA task design supported more sophisticated understandings of FA, including (1) an increased recognition of the pedagogical necessity of incorporating authentic FA tasks into lesson planning, (2) a growing aspiration to implement FA-oriented instruction that promotes higher-order thinking, (3) an enhanced awareness of the empowering role of FA tasks in fostering students’ self-regulated learning, and (4) a more nuanced understanding of the challenges inherent in implementing FA practices. Meanwhile, the case illustrates how pre-existing assessment conceptions, school culture norms, and limited targeted mentoring can constrain LAL development in relation to FA. By providing a fine-grained account of developmental processes, this study offers insights into how ITE can mediate student teachers’ engagement with FA task design. The findings have implications for teacher education programs in other similar educational contexts, particularly regarding the integration of FA task design into assessment courses and the provision of sustained, context-sensitive support during teaching practicum. Full article

The Transport Layer Security (TLS) protocol is widely used nowadays to create secure communications over TCP/IP networks. Its purpose is to ensure confidentiality, authentication, and data integrity for messages exchanged between two endpoints. In order to facilitate its integration into widely used applications, the protocol is typically implemented through libraries, such as OpenSSL, BoringSSL, LibreSSL, WolfSSL, NSS, or mbedTLS. These libraries encompass functions that execute the specialized TLS handshake required for channel establishment, as well as the construction and processing of TLS records, and the procedures for closing the secure channel. However, these software libraries may contain vulnerabilities or errors that could potentially jeopardize the security of the TLS channel. To identify flaws or deviations from established standards within the implemented TLS code, a specialized tool known as TLS-Anvil can be utilized. This tool also verifies the compliance of TLS libraries with the specifications outlined in the Request for Comments documents published by the IETF. TLS-Anvil conducts numerous tests with a client/server configuration utilizing a specified TLS library and subsequently generates a report that details the number of successful tests. In this work, we exploit the results obtained from a selected subset of TLS-Anvil tests to generate rules used for anomaly detection in Suricata, a well-known signature-based Intrusion Detection System. During the tests, TLS-Anvil generates .pcap capture files that report all the messages exchanged. Such files can be subsequently analyzed with Wireshark, allowing for a detailed examination of the messages exchanged during the tests and a thorough understanding of their structure on a byte-by-byte basis. Through the analysis of the TLS handshake messages produced during testing, we develop customized Suricata rules aimed at detecting TLS anomalies that result from flawed implementations within the intercepted traffic. Furthermore, we describe the specific test environment established for the purpose of deriving and validating certain Suricata rules intended to identify anomalies in nodes utilizing a version of the OpenSSL library that does not conform to the TLS specification. The rules that delineate TLS deviations or potential attacks may subsequently be integrated into a threat detection platform supporting Suricata. This integration will enhance the capability to identify TLS anomalies arising from code that fails to adhere to the established specifications. Full article

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike traditional scripted interactions, the system employs role-based prompt engineering to align real-time AI dialogue with the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) framework, enabling dynamic scaffolding and authentic misinformation scenarios. A mixed-method experiment with 60 undergraduate students compared this AI-driven approach to traditional instruction using a 40-item digital-literacy pre/post test, the Intrinsic Motivation Inventory (IMI), and open-ended reflections. Results indicated that while both groups improved significantly, the game-based group achieved larger gains in credibility-evaluation performance and reported higher perceived competence, interest, and effort. Qualitative analysis highlighted the HCI trade-off between the high pedagogical value of adaptive AI guidance and technical constraints such as system latency. The findings demonstrate that Generative AI can be effectively operationalized as a dynamic interface layer in serious games to strengthen critical reasoning. This study provides practical guidelines for architecting AI-NPC interactions and advances the theoretical understanding of AI-supported educational informatics. Full article

This study aims to examine the methodological applicability of the Theory of Inventive Problem Solving (TRIZ) in the conservation and revitalization of traditional military settlements. Using Zhenjing Village in Jingbian County as a case, the research constructs a systematic framework for contradiction identification and strategy generation. Methods: Through preliminary surveys, data integration, and system modeling, the study identifies major conflicts among authenticity preservation, ecological carrying capacity, and community vitality in Zhenjing Village. Technical contradiction matrices, separation principles, and the Algorithm of Inventive Problem Solving (ARIZ) are employed for structured analysis. Further, system dynamics modeling is used to simulate the effectiveness of strategies and to evaluate the dynamic impacts of various conservation interventions on authenticity maintenance, ecological stress, and community vitality. The research identifies three categories of core technical contradictions and translates the 39 engineering parameters into an indicator system adapted to the cultural heritage conservation context. ARIZ is used to derive the Ideal Final Result (IFR) for Zhenjing Village, which includes self-maintaining authenticity, self-regulating ecology, and self-activating community development, forming a systematic strategy. System dynamics simulations indicate that, compared with “inertial development,” TRIZ-oriented strategies reduce the decline in heritage authenticity by approximately 40%, keep ecological pressure indices below threshold levels, and significantly enhance the sustainability of community vitality. TRIZ enables a shift in the conservation of traditional military settlements from experience-driven approaches toward systematic problem solving. It strengthens conflict-identification capacity and improves the logical rigor of strategy generation, providing a structured and scalable innovative method for heritage conservation in arid and ecologically fragile regions in northern China and similar contexts worldwide. Full article