A Lightweight Authentication Scheme for Power IoT Based on PUF and Chebyshev Chaotic Map

With the wide application of IoT technologies in the power sector, power IoT faces serious security challenges, which can be severely affected by malicious attacks and unauthorised access. Meanwhile, devices in power IoT are usually resource-constrained and deployed in a decentralised manner, making them vulnerable to physical attacks. Therefore, a robust and reliable lightweight authentication scheme needs to be constructed to guarantee its information security. A lightweight authentication scheme for the power IoT based on Physical Unclonable Function (PUF) and Chebyshev chaotic map is proposed in this paper, which achieves two-way authentication and session key negotiation between gateways and terminal devices. Comparing with traditional authentication schemes, the PUF and Chebyshev chaotic map used in this scheme have high security and lower resource overhead. PUF is used to generate Challenge and Response Pairs (CRPs) for two-way authentication and key negotiation without storing any secret information about authentication in the device memory. At the same time, Chebyshev chaotic map is used to protect the transmission of secret information such as CRPs in insecure channels. The solution is therefore resistant to attacks such as physical, machine learning modelling and impersonation, ensuring the information security of the authentication process. The proposed scheme is analyzed and verified using the formal verification tool ProVerif and improved BAN logic along with informal methods. The verification results show that the scheme satisfies 12 security properties such as two-way authentication and user anonymity. Comparative analysis with existing related authentication schemes shows that the proposed scheme has low computation and communication costs while guaranteeing security, thus rendering it suitable for resource-constrained terminal devices in the power IoT.

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Extraction of Meta-Data for Recommendation Using Keyword Mapping

Expanding traditional video metadata and recommendation systems encompasses challenges that are difficult to address with conventional methodologies. Limitations in utilizing diverse information when extracting video metadata, along with persistent issues like bias, cold start problems, and the filter bubble effect in recommendation systems, are primary causes of performance degradation. Therefore, a new recommendation system that integrates high-quality video metadata extraction with existing recommendation systems is necessary. This research proposes the “Extraction of Meta-Data for Recommendation using keyword mapping,” which involves constructing contextualized data through object detection models and STT (Speech-to-Text) models, extracting keywords, mapping with the public dataset MovieLens, and applying a Hybrid recommendation system. The process of building contextualized data utilizes YOLO and Google’s Speech-to-Text API. Following this, keywords are extracted using the TextRank algorithm and mapped to the MovieLens dataset. Finally, it is applied to a Hybrid Recommendation System. This paper validates the superiority of this approach by comparing it with the performance of the MovieLens recommendation system that does not expand metadata. Additionally, the effectiveness of metadata expansion is demonstrated through performance comparisons with existing deep learning-based keyword extraction models. Ultimately, this research resolves the cold start and long-tail problems of existing recommendation systems through the construction of video metadata and keyword extraction.

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MCQGen: A Large Language Model-Driven MCQ Generator for Personalized Learning

In the dynamic landscape of contemporary education, the evolution of teaching strategies such as blended learning and flipped classrooms has highlighted the need for efficient and effective generation of multiple-choice questions (MCQs). To address this, we introduce MCQGen, a novel generative artificial intelligence framework designed for the automated creation of MCQs. MCQGen uniquely integrates a large language model (LLM) with retrieval-augmented generation and advanced prompt engineering techniques, drawing from an extensive external knowledge base. This integration significantly enhances the ability of the LLM to produce educationally relevant questions that align with both the goals of educators and the diverse learning needs of students. The framework employs innovative prompt engineering, combining chain-of-thought and self-refine prompting techniques, to enhance the performance of the LLM. This process leads to the generation of questions that are not only contextually relevant and challenging but also reflective of common student misconceptions, contributing effectively to personalized learning experiences and enhancing student engagement and understanding. Our extensive evaluations showcase the effectiveness of MCQGen in producing high-quality MCQs for various educational needs and learning styles. The framework demonstrates its potential to significantly reduce the time and expertise required for MCQ creation, marking its practical utility in modern education. In essence, MCQGen offers an innovative and robust solution for the automated generation of MCQs, enhancing personalized learning in the digital era.

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EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Intelligent applications supported by Machine Learning have achieved remarkable performance rates for a wide range of tasks in many domains. However, understanding why a trained algorithm makes a particular decision remains problematic. Given the growing interest in the application of learning-based models, some concerns arise in the dealing with sensible environments, which may impact users’ lives. The complex nature of those models’ decision mechanisms makes them the so-called “black boxes,” in which the understanding of the logic behind automated decision-making processes by humans is not trivial. Furthermore, the reasoning that leads a model to provide a specific prediction can be more important than performance metrics, which introduces a trade-off between interpretability and model accuracy. Explaining intelligent computer decisions can be regarded as a way to justify their reliability and establish trust. In this sense, explanations are critical tools that verify predictions to discover errors and biases previously hidden within the models’ complex structures, opening up vast possibilities for more responsible applications. In this review, we provide theoretical foundations of Explainable Artificial Intelligence (XAI), clarifying diffuse definitions and identifying research objectives, challenges, and future research lines related to turning opaque machine learning outputs into more transparent decisions. We also present a careful overview of the state-of-the-art explainability approaches, with a particular analysis of methods based on feature importance, such as the well-known LIME and SHAP. As a result, we highlight practical applications of the successful use of XAI.

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Optimal Operations of Local Energy Market With Electric Vehicle Charging and Incentives for Local Grid Services

The development of a local energy market (LEM) in Thailand involves prosumers and electric vehicle (EV) owners in a low-voltage distribution system (LVDS). However, both maximizing social welfare through independent energy transactions and managing distribution network constraints (DNCs) remain challenging. Additionally, defining fair incentives and ensuring equitable participation in local grid service programs further add to these challenges. This paper proposes an optimal LEM operation incorporating participants’ selection of energy transaction partners and independent price negotiations to maximize social welfare. The distribution system operator provides grid service programs to address violations, define fair incentives, ensure equitable participation, and guarantee the resolution of DNC violations while participants maintain control over their power consumption. A novel penalty scheme is proposed to support these grid service programs. Numerical simulations on a 380-V LVDS in Thailand demonstrate that social welfare is maximized at 19.17 THB/h across all preference cases during the study period. The LEM trades electricity without violating DNCs while allowing self-management. Results show that EVs contribute 83.66% of the demand reduction required by the distribution system operator, while the penalty scheme discourages 100% of individual benefit pursuit. Revenue compensation covers 100% of all prosumers’ revenue before implementing the grid service programs for all periods.

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Bending Strength Prediction of the Cu-Sn Alloy Through a Visual Quantization Model Integrated With Microstructure Characterization and Machine Learning

The multimodal properties of the grinding wheel matrix significantly impact grinding performance, while research on the interactions among these properties remains notably limited. To investigate the latent relationship between the microstructure and the bending strength of the bronze matrix, a visual quantization model based on the microstructure of the Cu-Sn alloy samples was established. The proposed model integrated a image segmentation network module, a quantitative characterization module, and a multivariate prediction module. The enhancement of the segmentation network is based on the synergistic combination of full-scale feature fusion with attention mechanism. The quantitative characterization parameters of metallographic microstructure features are proposed, and the most prominent intercorrelation between these parameters is studied from multiple dimensions. The results show that the modified image segmentation network exhibits superior performance compared to Unet, as evidenced by a 3% increase in Mean Intersection over Union (MIOU). The optimized output strategy ( ηDd -PSO-SVR) can contribute to the model’s prediction accuracy of material bending strength (MSE = 23.558,R 2=0.934 ). Finally, this work shows that the microscopic information demonstrates great adaptability for the machine learning models in predicting bending strength.

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How Environmental Leadership Promote Technological Innovation in Resource-Based Enterprises: The Role of Green Investment and Government Subsidy

Technological innovation represents the core driving force for the green development of resource-based enterprises. How to effectively promote enterprises’ technological innovation has received considerable attention in academia and practice. However, most of existing studies have been focused on the influence of industrial level or organizational level, the impact of leadership has received little attention. Therefore, this study explores the impact of environmental leadership, which focuses on environmental protection and sustainable development, on technological innovation. Using Stata 18 software, this study analyzes 170 resource-based enterprises listed on the Shenzhen and Shanghai A-share stock markets, for the time periods spanning from 2013 to 2022. The results indicate a positive impact of environmental leadership on technological innovation, with green investment mediating the relationship between environmental leadership and technological innovation. However, the moderating effect of government subsidies between environmental leadership and green investment is not significant. Our findings offer a better understanding of technological innovation in resource-based enterprises by considering the influence factors from both internal and external pespectives, especially the critical role of leader. Therefore, this study contributes to the literature on leadership theory and technological innovation theory.

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Robust Contact-Rich Task Learning With Reinforcement Learning and Curriculum-Based Domain Randomization

We propose a framework for contact-rich path following with reinforcement learning based on a mixture of visual and tactile feedback to achieve path following on unknown environments. We employ a curriculum-based domain randomisation approach with a time-varying sampling distribution, rendering our approach is robust to parametric uncertainties in the robot-environment system. Based on evaluation in simulation for compliant path-following case studies with a random uncertain environment, and comparison with LBMPC and FDM methods, the robustness of the obtained policy over a stiffness range 104 – 109 N/m and friction range 0.1–1.2 is demonstrated. We extend this concept to unknown surfaces with various surface curvatures to enhance the robustness of the trained policy in terms of changes in surfaces. We demonstrate ∼15× improvement in trajectory accuracy compared to the previous LBMPC method and ∼18× improvement compared to using the FDM approach. We suggest the applications of the proposed method for learning more challenging tasks such as milling, which are difficult to model and dependent on a wide range of process variables.

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StrikeNet: Deep Convolutional LSTM-Based Road Lane Reconstruction With Spatiotemporal Inference for Lane Keeping Control

This paper presents a Spatio-Temporal Road Inference for a KEeping NETwork (StrikeNet), aimed at enhancing Road Lane Reconstruction (RLR) and lateral motion control in Autonomous Vehicles (AV) using deep neural networks. Accurate road lane model coefficients are essential for an effective Lane Keeping System (LKS), but the traditional vision system often fails in situations where lane markers are absent or faint and cannot be properly recognized. To overcome this, a driving dataset was restructured, combining road information from a vision system and forward images for spatial training of RLR. Sequential spatial learning outputs were then processed with in-vehicle sensor data for temporal inference via Long Short-Term Memory (LSTM). The StrikeNet was rigorously tested in both typical and uncertain driving environments. Comprehensive statistical and visualization analyses were conducted to evaluate the performance of various RLR methods and lateral motion control strategies. Remarkably, the RLR demonstrated its capability to derive reliable road coefficients even in the absence of lane markers. Upon performance comparison with four alternative techniques, our method yields the lowest error and variance between human steering inputs and the control input. Specifically, under high and low lane quality conditions, the proposed method maximally reduced the control input error by up to 72% and 66%, respectively, and decreased the variance by 54% and 94%, respectively. The findings highlight StrikeNet’s effectiveness in bolstering the fail-operational performance, and reliability of lane-keeping or lane departure warning systems in autonomous driving, thereby enhancing control continuity and mitigating path deviation-induced traffic accidents.

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Mobile Software Development Kit for Real Time Multivariate Blood Glucose Prediction

In the field of blood glucose prediction, the literature is abounded with algorithms that demonstrate potential in glucose management. However, these propositions face an issue common to many machine learning algorithms: the repeated reuse of datasets (overfitting) and a tendency to develop algorithms in isolation, detached from practical scenarios. Compounding these challenges is that many insulin pump vendors and continuous glucose monitor vendors use closed and proprietary protocols, restricting researchers’ data access and the ability to deploy complex, multivariate optimizers. This study seeks to bridge the gap between theoretical algorithms and their real-world applications by devising a software development kit. This kit collects real-time data from continuous glucose monitors, carbohydrate intake, insulin deliveries from insulin management systems, and metrics like physical activity, stress, and sleep from wearables. Our methodology leverages the open-source insulin management system, Loop, integrated with Apple Health and various wearable devices. Although navigating through diverse communication protocols to link these devices presented challenges, we succeeded in aggregating a comprehensive dataset for blood glucose predictions. To underscore the utility of our software development kit, we executed a technical proof-of-concept on this platform, illustrating real-time, individualized, data-driven multivariate blood glucose predictions. We hope that our platform can contribute to transforming machine learning algorithms from technical developments into actionable tools with real-world benefits in blood glucose management. It provides a foundation for researchers to refine their predictive algorithms and decision support systems within a more dynamic, data-rich environment.

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