Search Results (1,337)

Searchable encryption (SE) allows users to efficiently retrieve data from encrypted cloud data, but most of the existing SE solutions only support precise keyword search. Fuzzy searchable encryption agrees with practical situations well in the cloud environment, as search keywords that are misspelled to some extent can still generate search trapdoors that are as effective as correct keywords. In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. For example, in medical application scenarios where malicious cloud servers may exist, diverse types of files need to correspond to doctors in the corresponding departments, and there is a lack of fine-grained access control for sharing decryption keys for different types of files. In the application of medical cloud storage, malicious cloud servers may return incorrect ciphertext files. Since diverse types of files need to be guaranteed to be accessible by doctors in the corresponding departments, sharing decryption keys with the corresponding doctors for different types of files is an issue. To solve these problems, a verifiable fuzzy searchable encryption with blockchain-assisted multi-user scenarios is proposed. Locality-sensitive hashing and bloom filters are used to realize multi-keyword fuzzy search, and the bigram segmentation algorithm is optimized for keyword conversion to improve search accuracy. To realize fine-grained access control in multi-user scenarios, ciphertext-policy attribute-based encryption (CP-ABE) is used to distribute the shared keys. In response to the possibility of malicious servers tampering with or falsifying users’ search results, the scheme leverages the blockchain’s technical features of decentralization, non-tamperability, and traceability, and uses smart contracts as a trusted third party to carry out the search work, which not only prevents keyword-guessing attacks within the cloud server, but also solves the verification work of search results. The security analysis leads to the conclusion that the scheme is secure under the adaptively chosen-keyword attack. Full article

Due to the openness of communication channels and the sensitivity of the data being collected and transmitted, securing data access and communication in IoT systems requires robust ECC-based authentication and key agreement (AKA) protocols. However, designing an AKA protocol for IoT presents significant challenges, as most IoT sensors are deployed in resource-constrained, unattended environments with limited computational power, connectivity, and storage. To achieve anonymous authentication, existing solutions typically rely on shared temporary public keys to mask device IDs or validate sender certificates, which increases the computational overhead. Furthermore, these protocols often fail to address crucial security concerns, such as nonresistance to ephemeral secret leakage (ESL) attacks and a lack of perfect forward security. To mitigate the computational burden, we propose a dynamic authenticated credentials (DACs) synchronization framework for anonymous authentication. Then, we introduce an ECC-based AKA scheme that employs DACs in place of temporary public keys or sender credentials, enabling efficient and secure anonymous authentication. The security of the proposed protocol was rigorously verified under the Real-or-Oracle model and validated using ProVerif. Performance comparisons demonstrate that our scheme offered significant improvements in security, with an over 37% reduction in communication cost and computational overhead. Full article

The emergence of Generative Artificial Intelligence (GenAI) raises critical questions about learner autonomy and agency. This exploratory case study examines how four university-level German language learners with diverse backgrounds developed autonomy in their learning process through engagement with AI tools. The study was conducted in early 2023 when most learners were first discovering ChatGPT’s potential for language learning. Data were collected through reflective journals, digital portfolios, and interviews during a semester-long course that scaffolded self-directed learning with AI integration. The findings reveal emerging patterns of shared agency between learners and AI tools. Learners developed distinct strategies for AI integration based on their language learning backgrounds, with heritage speakers focusing on accuracy improvement while classroom learners emphasized communication practice. Cross-case analyses identified key dimensions of autonomy development: a critical evaluation of AI output, evolving learner–AI relationships, maintaining and developing a second language (L2) voice, and the strategic integration of AI tools while preserving learner agency. These patterns suggest that autonomy in AI-mediated environments manifests through learners’ capacity to engage productively with AI while maintaining critical awareness and personal agency in their learning process. Full article

The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought about a serious loss of financial institutions’ ability to do business. In small sample data environments, traditional fraud detection methods based on prototype network models struggle with the loss of time-series features and the challenge of identifying the uncorrected sample distribution in the metric space. In this paper, we propose a credit card fraud detection method called the Time-Series Attention-Boundary-Enhanced Prototype Network (TABEP), which strengthens the temporal feature dependency between channels by incorporating a time-series attention module to achieve channel temporal fusion feature acquisition. Additionally, nearest-neighbor boundary loss is introduced after the computation of the prototype-like network model to adjust the overall distribution of features in the metric space and to clarify the representation boundaries of the prototype-like model. Experimental results show that the TABEP model achieves higher accuracy in credit card fraud detection compared to five existing baseline prototype network methods, better fits the overall data distribution, and significantly improves fraud detection performance. This study highlights the effectiveness of open innovation methods in addressing complex financial security problems, which is of great significance for promoting technological advancement in the field of credit card security. Full article

Achieving climate neutrality has become an environmental goal for all European Union (EU) Member States. Thanks to numerous projects and subsidies, EU countries are able to achieve the goal of decarbonizing energy sources. The aim of the presented study is to assess the validity of Member States’ actions to reduce CO₂ emissions based on data provided by Eurostat. The presented study combines, using regression, data on financial outlays dictated by environmental protection, the share of renewable energy, and total CO₂ emissions in individual Member States. This study has shown that, despite differences in the use of energy sources, Member States are able to achieve economic growth in symbiosis with the environment. Full article

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins. Full article

Urbanization presents significant challenges to disaster management as cities grow and develop, hence increasing their vulnerability to disasters. Disaster resilience is crucial for protecting lives and infrastructure, ensuring economic stability, promoting equality and cohesion, and ensuring the long-term viability of metropolitan regions in these rapidly growing cities. This paper investigates contemporary approaches to creating smart and resilient urban environments through disaster management that emphasize community-based solutions in prioritizing advanced technologies. The key findings of the research include three factors to be accomplished in utilizing technology in community-based disaster management, trust in the crowd, digital divide, and cultural sensitivity. Moreover, the review highlights the significance of the use of smart technologies in improving urban resilience, including but not limited to real-time data-sharing platforms and ML algorithms. Furthermore, it emphasizes the challenges regarding reliability and accuracy in crowdsourced information, stressing the importance of user awareness. Full article

This work introduces a novel custom-designed 16-bit RISC-V processor, intended for educational purposes and for use in low-resource equipment. The implementation, despite providing registers of 16 bits, is based on RV32E RISC-V ISA, but with some key differences like a reduced instruction set that is optimized for embedded systems, the removal of floating-point instructions, reduced register count, and modified data types. These changes enable the processor to operate efficiently in resource-constrained environments while still maintaining assembly-level compatibility with the standard RISC-V architecture. The educational aspects of this project are also a key focus. By working on this project, students can gain hands-on experience with digital logic design, Verilog programming, and computer architecture. The project also includes tools and scripts to help students transform assembly code into binary format, making it easier for them to test and verify their designs. Additionally, the project’s open-source nature allows for collaboration and the sharing of knowledge among students and researchers worldwide. Full article

The emergence of dockless bicycle sharing has transformed urban transportation, particularly in China, by offering a flexible and accessible travel option. However, understanding the factors driving its adoption and usage in disadvantaged neighborhoods is crucial, as these areas often face unique mobility challenges. This study explores these determinants, providing a more comprehensive analysis than prior research by focusing specifically on disadvantaged communities. Using survey data from four such neighborhoods in Xi’an, China, we apply Structural Equation Modeling to investigate the factors influencing individuals’ decisions to adopt and intensively use dockless bicycle sharing for commuting and errands. The results reveal key determinants, including psychological factors, demographic characteristics, and spatial and social contexts, and their interaction mechanisms. Attitudes are found to have a substantial impact on bicycle-sharing behavior for both commuting and errands, while social norms and perceived behavioral control (PBC) mainly influence usage for errands. Interestingly, PBC affects adoption but not usage frequency. The findings also highlight that proximity to schools, subways, and neighborhood aesthetics positively correlate with bicycle-sharing adoption for errands, whereas bicycling infrastructure significantly influences usage intensity. However, none of the neighborhood environment factors were found to significantly affect adoption for commuting purposes. These insights are especially valuable for developing targeted strategies to promote bicycle sharing as a sustainable transportation solution in disadvantaged neighborhoods, where improved access can significantly enhance mobility and quality of life. Full article

The integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) is a growing reality in the building production sector. Through this integration, it is possible to improve the efficiency of management, maintenance, use and planning of conservation operations, providing an integrated and dynamic vision of the built environment. Simultaneous exchange of BIM-GIS elements in a shared environment facilitates information access and optimizes processes like requalification, activity planning, safety and sustainable urban design. Two alternative strategies are proposed for the multidisciplinary approach, using advanced technologies to acquire, process and manage detailed and georeferenced data. The first one is an open-source environment to guarantee flexibility, customization and accessibility. The second option, in a closed-source environment, provides advanced functionalities and dedicated support. Both require careful planning, detailed analysis and collaboration between the disciplines of architecture, engineering and geoinformatics. The study transcends theoretical analysis by exploring practical implications for real-world systems integration, examining their advantages, limitations and potential synergies in terms of flexibility, security and sustainability. This will enable a more efficient and comprehensive management of the architectural heritage and the built environment, contributing to its preservation and enhancement in the context of the digital transition in a future perspective of smart cities. Full article

With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge intelligence. In this context, federated learning, as a new distributed machine learning method, becomes one of the key technologies to realize edge intelligence. Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. However, this fixed infrastructure is difficult to adapt to the complex and ever-changing heterogeneous network environment. With its high degree of flexibility and mobility, the introduction of unmanned aerial vehicles (UAVs) into the federated learning framework can provide enhanced communication, computation, and caching services in edge intelligence networks, but the limited communication bandwidth and unreliable communication environment increase system uncertainty and may lead to a decrease in overall energy efficiency. To address the above problems, this paper designs a UAV-assisted federated learning with a privacy-preserving and efficient data sharing method, Communication-efficient and Privacy-protection for FL (CP-FL). A network-sparsifying pruning training method based on a channel importance mechanism is proposed to transform the pruning training process into a constrained optimization problem. A quantization-aware training method is proposed to automate the learning of quantization bitwidths to improve the adaptability between features and data representation accuracy. In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. Experiments on real-world datasets validate the effectiveness of the scheme. The experimental results show that compared with other federated learning frameworks, the CP-FL approach can effectively mitigate the communication overhead, as well as the computation overhead, and has the same outstanding advantage in terms of the balance between privacy and usability in differential privacy preservation. Full article

Resilience has become a focal point of academic research investigating the impact of adverse disruption to the well-being of people, systems, the built environment, ecosystems, and climate. However, the proliferation of this work has not been accompanied by increasing clarity about the core meaning of resilience as a singular construct, threatening its relevance and complicating its use in practice. To improve the application of resilience in cross-disciplinary and convergence approaches to sustainability and well-being research, this work synthesized resilience conceptualizations across disciplines with novel artificial intelligence (AI)-augmented approaches. Using open-source applications for text mining and machine-learning-based natural language processing algorithms for the examination of text-as-data, this work mapped the content of 50 years of academic resilience work (24,732 abstracts). Presented as thematic and statistical textual associations in a series of network maps and tables, the findings highlight how specific measurements, components, and terminologies of resilience relate to one another within and across disciplines, emphasizing what concepts can be used to bridge disciplinary boundaries. From this, a converged conceptualization is derived to answer theoretical questions about the nature of resilience and define it as a dynamic process of control through the stages of disruption and progression to an improved state thereafter. This conceptualization supports a cross-disciplinary meaning of resilience that can enhance its shared understanding among a variety of stakeholders, and ultimately, the rigor and uniformity of its application in addressing sustainability and well-being challenges across multiple domains. Full article

Background/Objectives: In the UK, significant and rising numbers of children arrive in schools with marked deficits in key skills such as oral language. This rise has been further negatively impacted by the COVID-19 pandemic. Given this, the foundation phase of primary school education is a necessary environment for targeting language deficits. There is evidence to suggest that teaching assistant (TA)-led interventions can be effective when adequate training and support are provided. This study explored the feasibility of providing a brief, online dialogic book-sharing training to TAs, and whether this training would be effective in upskilling TAs and enable them to improve the language outcomes of children aged 3–7 years in a school context. Methods: North Wales primary schools were invited to nominate TAs for the two half-day training sessions. Five schools responded, and eleven TA–child dyads participated. Data were collected on recruitment, training acceptability and baseline, and post-training measures from TAs and children (2–3 weeks after the final training session) and 4–6 weeks after the first follow-up. Measures of TA competence and behavior were collected, along with measures of child language and behavior. Results: Schools and TAs were recruited; TAs reported positively to the training, and the results showed small to large effect size benefits on all TA skills and child expressive language with significant positive post-training effects on TAs’ use of reflections and child language abilities. However, these effects were somewhat reduced at follow-up. Conclusions: Overall, the results of this feasibility study provide positive evidence for this training as an accessible way for schools to strengthen their prevention infrastructures by professionalizing a growing, but relatively untrained, group within the school workforce. Full article

Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. This study examines the energy, economic, and environmental effects of CPEC’s energy investments in Pakistan, covering a range of power sources, including coal, hydro, solar, wind, and nuclear energy. Utilizing data from 31 CPEC energy projects and employing the GTAP-E-Power model, this research assesses these impacts through seven scenarios, comprehensively analyzing the heterogeneity of different power sources. Our findings reveal that while all types of CPEC energy infrastructure investments contribute to increasing the share of zero-emissions electricity to 49.1% and reducing CO₂ emissions by 18.61 million tons, the economic impacts vary significantly by energy source. The study suggests that it is crucial to prioritize renewable energy investments while addressing immediate power shortages to balance economic growth with environmental sustainability. Policymakers should also consider the potential inter-sectoral substitution effects when applying significant shocks to specific sectors. This analysis informs future energy investment decisions under CPEC and offers insights for other Belt and Road Initiative (BRI) countries aiming to optimize their energy strategies for sustainable development. Full article

The idea of introducing a robot into an Ambient Assisted Living (AAL) environment to provide additional services beyond those provided by the environment itself has been explored in numerous projects. Moreover, new opportunities can arise from this symbiosis, which usually requires both systems to share the knowledge (and not just the data) they capture from the context. Thus, by using knowledge extracted from the raw data captured by the sensors deployed in the environment, the robot can know where the person is and whether he/she should perform some physical exercise, as well as whether he/she should move a chair away to allow the robot to successfully complete a task. This paper describes the design of an Ambient Assisted Living system where an IoT scheme and robot coexist as independent but connected elements, forming a cyber-physical system-of-systems architecture. The IoT environment includes cameras to monitor the person’s activity and physical position (lying down, sitting…), as well as non-invasive sensors to monitor the person’s heart or breathing rate while lying in bed or sitting in the living room. Although this manuscript focuses on how both systems handle and share the knowledge they possess about the context, a couple of example use cases are included. In the first case, the environment provides the robot with information about the positions of objects in the environment, which allows the robot to augment the metric map it uses to navigate, detecting situations that prevent it from moving to a target. If there is a person nearby, the robot will approach them to ask them to move a chair or open a door. In the second case, even more use is made of the robot’s ability to interact with the person. When the IoT system detects that the person has fallen to the ground, it passes this information to the robot so that it can go to the person, talk to them, and ask for external help if necessary. Full article