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SuRaksha: AI-powered blockchain framework for HVAC tamper detection and authentication in smart classrooms
Full text linkSmart Classrooms (SCR) are reshaping the learning experience with their interactive technology and personalized knowledge, leading to improved student engagement by seamlessly integrating digital gadgets. In this rapidly evolving landscape, the integrity and efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems are essential to the futuristic student's life. This paper introduces SuRaksha: AI-Powered Blockchain Framework for HVAC Tamper Detection and Authentication in Smart Classrooms. Leveraging the power of ensemble learning (EL) at the intelligent and connection layer of the proposed framework, our approach employs IoT sensors to collect comprehensive data from HVAC appliances. The EL algorithms then analyze the collected data to detect any instances of tampering in real-time. At the security layer, robust authentication mechanisms are implemented to verify the integrity of the HVAC data before securely storing it on the cloud. This multi-layered framework enhances the detection and authentication processes and ensures the reliability and security of HVAC operations in intelligent classroom environments. Extensive experiments and validations demonstrate the efficacy of the proposed framework in identifying tampering incidents and providing a secure, authenticated, and reliable system for modern educational facilities. The validation outcomes of the proposed framework demonstrate excellent performance, with an average processing time of 3.725 Secs and 99.84% accuracy for Smart Classrooms compared to existing works
Generative artificial intelligence for sustainable learning development: exploring students' perspectives in higher education
No full textResearch indicates that generative artificial intelligence (Gen AI) and its integration into higher education (HE) classes can support the developments in sustainability, and lead to improvements in educational practices, promoting education for social justice, fostering inclusion and accessibility for all students, irrespective of their background, and promoting environmental, i.e., climate awareness and sustainability. These are only some of the ways Gen AI can support and enhance the creation of more effective, inclusive, and sustainable contexts in HE. The current study aimed to explore students' perspectives on Gen AI tools to inform changes that should be implemented in HE to support the more effective use of Gen AI tools by students. One hundred sixty-three students from universities in Greece, Albania, and the UK participated in this study. It is suggested that HE policies and regulations should be modified and reformulated to support sustainable development, while empirical research and its incorporation in HE is a necessity (there is limited focus on practice-oriented/empirical research)
The effective use of generative AI in higher education exploring lecturers' artificial intelligence literacy: unveiling sustainable and ethical AI-powered practices for learning, teaching, and assessment
Full text linkGenerative artificial intelligence has transformed the way educators deliver their lectures and seminars in higher education institutions around the world having an impact on societal norms as well. Despite the impact the adoption of GenAI has had on lecturers and students in higher education, very few research studies have explored lecturers' perceptions of Gen AI and identified sustainable and ethical practices of Gen AI for learning, teaching, and assessment. The current study explored 42 European lecturers' perceptions of the use of innovative Gen AI tools. The current qualitative study used semi-structured interviews to collect data. Convenience sampling was used in this study as the researcher tapped on her network to invite as many lecturers as possible to participate in this study. The data were transcribed and analyzed using the deductive approach for the thematic analysis. The findings revealed that lecturers had an overall positive attitude to adopt and use AI for content creation, debates, assessment and feedback, and conducting research in their respective institutions
Transfer learning based gender identification using arbitrary celebrity image sets
Full text linkGender Identification is important for security, personalization, and social media analysis, where accurate gender identification enhances the system performance. In this study, we investigated the use of transfer learning for Gender identification from the perspective of minimizing accuracy and efficiency loss. The models, pre-trained on ImageNet weights, were fine-tuned on 300 celebrity images to analyze their performance with varied data constraints. The models were evaluated based on the model architecture and hyperparameters, such as batch size and data split. VGG16 and VGG19 worked impressively with a combined performance level of 98% (97% for female samples and 99% for male samples). However, ResNet50 and ResNet101 showed fluctuating levels of performance, attaining the best accuracy levels of 77.5% and 79.5%, respectively. The results indicate that less complex models, such as VGG16 and VGG19, outperform their more complex versions, such as ResNet50 and ResNet101, on smaller datasets because of their higher efficiency and suitability. More complex models require more sophisticated fine-tuning procedures but tend to have lower performance levels than less complex models. The research identifies that transfer learning significantly eliminates the necessity for longer retraining and customization of models, especially when adapting them to similar tasks. Additionally, considerable performance differences between the male and female categories were identified, highlighting the necessity of balanced datasets and model training to accurately reflect varied gender expressions
Digital marketing strategies for value co-creation (2nd edition): models and approaches for online brand communities
No full textAmidst growing conceptual developments in the areas of value co-creation and digital marketing, the importance of Online Brand Communities (OBCs) has emerged to reinforce strategies. This book provides an introduction to a range of broad and debatable conceptual perspectives and mechanisms on the subject of OBC. Focusing on contemporary digital marketing issues, it offers a comprehensive examination of consumers’ response to active engagement in such communities.
Building on the very successful original publication, this thoroughly revised second edition includes two new chapters on data-driven segmentation and artificial intelligence and customer engagement. The book balances theory with practical approaches and gives serious treatment to an important area of digital marketing strategy, providing an important resource for scholars, students and practitioners
(En)gendering change: understanding the gendered dynamics of domestic abuse perpetrator programmes
Full text linkDrawing on extensive participant observation and interviews, this article considers the interactive dynamics of two group based, probation domestic abuse perpetrator programmes. Specifically, the Integrated Domestic Abuse Programme (IDAP) and the Building Better Relationships Programme (BBR). Perpetrator groups are understood as involving collective emotions and understandings, which are continuously constructed and reconstructed through interactions. These interactions are highly gendered; reflecting men’s desires to present acceptable masculine identities and narratives, which they perceive as being threatened by their presence on a perpetrator programme. This article considers how gendered interactions take place within perpetrator groups, and calls for consideration of how they can support or undermine programme efficacy, and narratives of desistance
Dual-band multi-layer antenna array with circular polarization and gain enhancement for WLAN and X-band applications
Full text linkThis paper presents a novel multi-layer, dual-band antenna array designed for WLAN and X-band applications, incorporating several innovative features. The design employs a pentagon-shaped radiating element with parasitic strips to enable dual-band operation. A dual-transformed feed network with chamfered feed strip corners minimizes radiation distortion and cross-polarization while introducing orthogonal phase shifts to achieve circular polarization (CP) at the X-band. A Fabry–Pérot structure, strategically placed above the array, enhances gain in the WLAN band. The antenna demonstrates an impedance bandwidth of 1.8 GHz (S11 < −10 dB) at the WLAN band, with 36% fractional bandwidth, and 4.3 GHz at the X-band, with 43% fractional bandwidth. Measured peak gains are 7 dBi for the WLAN band and 6.8 dBi for the X-band, with favourable S11 levels, omni-directional radiation patterns, and consistent gain across both bands. Circular polarization is achieved within 8.5–10.4 GHz. Experimental results confirm the array’s significant advancements in multi-band performance, making it highly suitable for diverse wireless communication applications
Advancing cyber incident timeline analysis through retrieval-augmented generation and large language models
Full text linkCyber timeline analysis or forensic timeline analysis is critical in digital forensics and incident response (DFIR) investigations. It involves examining artefacts and events---particularly their timestamps and associated metadata---to detect anomalies, establish correlations, and reconstruct a detailed sequence of the incident. Traditional approaches rely on processing structured artefacts, such as logs and filesystem metadata, using multiple specialised tools for evidence identification, feature extraction, and timeline reconstruction.
This paper introduces an innovative framework, GenDFIR, a context-specific approach powered via large language model (LLM) capabilities. Specifically, it proposes the use of Llama 3.1 8B in zero-shot, selected for its ability to understand cyber threat nuances, integrated with a retrieval-augmented generation (RAG) agent.
Our approach comprises two main stages:
(1) Data preprocessing and structuring: incident events, represented as textual data, are transformed into a well-structured document, forming a comprehensive knowledge base of the incident.
(2) Context retrieval and semantic enrichment: a RAG agent retrieves relevant incident events from the knowledge base based on user prompts. The LLM processes the pertinent retrieved context, enabling a detailed interpretation and semantic enhancement. The proposed framework was tested on synthetic cyber incident events in a controlled environment, with results assessed using DFIR-tailored, context-specific metrics designed to evaluate the framework’s performance, reliability, and robustness, supported by human evaluation to validate the accuracy and reliability of the outcomes. Our findings demonstrate the practical power of LLMs in advancing the automation of cyber-incident timeline analysis, a subfield within DFIR. This research also highlights the potential of generative AI, particularly LLMs, and opens new possibilities for advanced threat detection and incident reconstruction
A politics of one’s own: leisure, belonging and momentary self-exclusion among British Bangladeshi women in East London
Full text linkThis paper investigates how leisure activities inform identification processes among British Bangladeshi Muslim women in Tower Hamlets, London. Focusing on women-only events organised in community centres that cater to British Bangladeshi women, we explore the significance of these spaces in the negotiation and maintenance of identity and community. Based on a two-year ethnography conducted as part of the research project Migrant Memory and Postcolonial Imagination, we argue that women-only leisure activities are part of a strategy of momentary self-exclusion, which is central to the articulation of a politics of location for participating women. The focus on leisure contributes to the literature on diaspora studies by providing a more holistic understanding of questions of belonging
Wideband circularly-polarized hexagonal SIW cavity-backed slot antenna with enhanced bandwidth and compact design
Full text linkThis paper presents a novel wideband circularly polarized (CP) cavity-backed slot antenna based on Substrate Integrated Waveguide (SIW) technology, designed for compact and high-efficiency performance. The proposed antenna utilizes a hexagonal SIW cavity to simultaneously excite two closely spaced resonant modes (TM110 and TM210), resulting in enhanced bandwidth for linear polarization (LP). To achieve circular polarization, a passive, single-layer linear-to-circular polarization converter is integrated above the cavity, offering a structurally simple and PCB-compatible solution. Unlike conventional CP designs that rely on complex feeding networks or multilayered structures, this configuration maintains a planar profile and efficient performance. A fabricated prototype demonstrates strong agreement between simulation and measurement, achieving a peak gain of 9.2 dBic and a 14% axial ratio (AR) bandwidth. These results highlight the antenna’s suitability for modern wireless systems requiring wideband CP functionality, including satellite communications, 5G modules, and compact embedded devices