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关键词： Internet of Things   Cloud computing   Servers   Authentication   Logic gates   Protocols   Security   Real-time systems   Edge computing   Computer science   Authentication protocol   blockchain   fog computing   Internet of Things (IoT) devices   Scyther tool   security and privacy  

摘要： The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users, IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user's identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (Scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications.

关键词： 黑神话:悟空   中华优秀传统文化   出圈   守正创新  

摘要： 作为以中华优秀传统文化为内核的“第九艺术”,首款国产3A游戏《黑神话:悟空》在全球范围内的强势“出圈”,既是对民族文化自信自强、文化产业高质量发展、大国文化“软实力”的生动剪影,也是基于对守正创新的核心逻辑和实践遵循的深刻把握,为传承文化“双创”的历史使命提供了有益思路。新时代新征程,推动中华优秀传统文化焕发新光彩:方向上,要挖掘人文精神内核、坚定文化自信,实现囿于主流生态题材的价值守正;表达上,要从围绕优化文化供给内容、巧用前沿数字技术、发挥产业联动思维实现突破圈层隔阂的叙事变革;传播上,要从聚焦提升话语认同的温度、提升话语传达的效度、提升话语影响的广度来实现跨越文化鸿沟的话语体系建构。

关键词： 黑神话·悟空   中华优秀传统文化   理想信念教育   数字游戏  

摘要： 以《黑神话·悟空》为例,探讨中华优秀传统文化题材数字游戏在理想信念教育中的作用。面对全球化和数字化的挑战,中华优秀传统文化与理想信念教育面临困境。通过分析该游戏的文化符号、叙事结构和互动设计,揭示其如何增强文化认同并塑造理想信念。建议未来游戏深化文化内涵、提升互动性与沉浸感,完善审核机制,以更好发挥教育作用。

关键词： Gender disparity   CS education   Systematic review   Computational thinking   K-12 education  

摘要： This study conducts a systematic review of the literature to investigate gender disparities in K-12 computer science (CS) education. We selected articles published in peer-reviewed journals that focused on both cognitive and affective aspects of computational thinking and CS education at the primary and secondary levels. A total of 29 studies were reviewed, and their findings were synthesized to assess the status of gender disparities, contributing factors, and instructional approaches. The results indicated that gender differences in cognitive and affective dimensions of CS education become more evident as students advance through school, with a greater negative impact on girls. The review identified three interconnected factors contributing to these disparities: socio-cultural influences, educational environments, and individual psychological factors. Additionally, the study proposed instructional approaches to address these issues and recommended future research to fill the current gaps in CS education research.

关键词： Recommender systems   Accuracy   User experience   Training   Resource management   Germanium   Electronic mail   Distance measurement   Data mining   Computer science   Valid coverage   item perspective   recommendation system  

摘要： Today, mainstream recommendation systems have achieved remarkable success in recommending items that align with user interests. However, limited attention has been paid to the perspective of item providers. Content providers often desire that all their offerings, including unpopular or cold items, are displayed and appreciated by users. To tackle the challenges of unfair exhibition and limited item acceptance coverage, we introduce a novel recommendation perspective that enables items to "select" their most relevant users. We further introduce ItemRec, a straightforward plug-and-play approach that leverages mutual scores calculated by any model. The goal is to maximize the recommendation and acceptance of items by users. Through extensive experiments on three real-world datasets, we demonstrate that ItemRec can enhance valid coverage by up to 38.5% while maintaining comparable or superior recommendation quality. This improvement comes with only a minor increase in model inference time, ranging from 1.5% to 5%. Furthermore, when compared to thirteen state-of-the-art recommendation methods across accuracy, fairness, and diversity, ItemRec exhibits significant advantages as well. Specifically, ItemRec achieves an optimal balance between precision and valid coverage, showcasing an efficiency gain ranging from 1.8 to 45 times compared to other fairness-oriented methodologies.

关键词： Protocols   Artificial intelligence   Systematic literature review   Computational modeling   Guidelines   Ethics   Computer science   Collaboration   Training   Standards  

摘要： Research protocol papers, scoping reviews, and systematic reviews are important tools for ensuring transparency, methodological rigor, accountability, and reproducibility in scholarly research. While widely adopted in disciplines such as medicine and social sciences, these practices remain underutilized in computer science and engineering, particularly within responsible AI research. This article explores the critical role that scoping and systematic reviews play in synthesizing knowledge, identifying gaps, and guiding future research in responsible AI. It also examines how research protocol papers-especially those developed for structured reviews-can improve the credibility, clarity, and replicability of responsible AI studies. By outlining the benefits and challenges of these methods, we argue for their greater adoption in AI ethics and responsible AI research. We conclude with recommendations to support the institutional, editorial, and cultural shifts necessary to integrate these rigorous methodological tools into responsible AI scholarship.

关键词： Vehicle routing   Fuels   Routing   Carbon dioxide   Traffic congestion   Smart cities   Optimization   Computer science   Vehicle dynamics   Load modeling   Crowd delivery   carbon emission   congestion   multi-depot rides   traffic management  

摘要： In the era of crowd delivery vehicle routing and traffic management in smart cities, a complex challenge appears indistinctly, affecting both developed and developing nations worldwide. This challenging problem involves optimizing multi-depot routes while addressing various hurdles: minimizing travel time, distance, fuel consumption, and carbon emissions, all while navigating dynamic traffic congestion across diverse pathways. Existing approaches often focus on isolated aspects like shortest paths, carbon emissions, or traffic prediction, leaving the comprehensive multi-depot traffic management problem unaddressed. In response, this research work proposes an Intelligent Multi-Depot Vehicle Routing and Management (IM-VRM) model which provides a comprehensive and holistic solution. It employs a Graph Neural Network (GNN) learning-based routing with a greedy optimization to establish initial optimal pathways for multi-depot journeys. Subsequently, the IM-VRM model integrates traffic congestion prediction with green parameter computation, engaging the Dijkstra algorithm to select the most admissible routes. This consecutive steps-based travel route guidance process optimizes routing for heterogeneous vehicles, including both heavy-duty and light-duty types. It accounts for load-dependent fuel consumption, velocity, and carbon emissions. By doing so, it simplifies the complexities of multi-depot traffic routing and management. The proposed model has been rigorously evaluated using a real-world multi-depot traffic dataset, demonstrating its practical viability. Notably, IM-VRM model achieves a remarkable improvement in fuel savings, reduced carbon emissions, and shorter travel time outperforming previous state-of-the-art methods in both efficiency and precision.

关键词： Recruitment   Security   Data collection   Crowdsensing   Internet of Things   Vehicle dynamics   Smart cities   Computer science   Benchmark testing   Next-generation crowdsensing network   zero-trust   data privacy   data quality   worker recruitment   worker recruitment  

摘要： Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for uncrewed aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches

关键词： Optimization   Approximation algorithms   Bandwidth   Scheduling   Scheduling algorithms   Schedules   Computer science   Mobile computing   Minimization   Lower bound   Age of Information (AoI)   average AoI   directional charging   wireless-powered network  

摘要： Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. First, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period T and the charging orientations assigned to each time slot within T. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI.