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关键词： inclusion   gender   computer science   secondary education  

摘要： There are increasing expectations that we should live in a digitally and computationally literate society. For many young people, particularly girls, school is the one place that provides an opportunity to develop the necessary knowledge and skills. This environment can either perpetuate and reinforce or eliminate existing gender inequalities. In this article, we present the "PLAYING, ENGAGEMENT, CREATVITIY, CREATING" (PECC) Framework, a practical guide to supporting teachers in the design of gender-sensitive learning activities, bringing students' own interests to the fore. Through a six-year, mixed-methods, design-based research approach, PECC-along with supporting resources and digital tools-was developed through iterative cycles of theoretical analysis, empirical data (both qualitative and quantitative), critical reflection, and case study research. Exploratory and instrumental case studies investigated the promise and limitations of the emerging framework, involving 43 teachers and 1453 students in secondary-school classrooms (including online during COVID-19) in Austria, Germany, and Switzerland. Quantitative data (e.g., surveys, usage metrics) and qualitative findings (e.g., interviews, observations, classroom artefacts) were analyzed across the case studies to inform successive refinements of the framework. The case study results are presented alongside the theoretically informed discussions and practical considerations that informed each stage of PECC. PECC has had a real-world, tangible impact at a national level. It provides an essential link between research and practice, offering a theoretically informed and empirically evidenced framework for teachers and policy makers.

关键词： Computer science   Ethics   Machine learning  

摘要： The majority of papers and patents in a subfield of AI enable surveillance, according to a new study.

关键词： Computer science   Engineering   Physics  

关键词： depression   cardiovascular disease   mental disorders   stroke   life satisfaction   cognitive ability  

摘要： Introduction Mental health problems, particularly depression, are intricately linked to chronic diseases and significantly impact overall health outcomes. This study aims to investigate the dose-response relationship between depression severity and risks of heart disease, stroke, and other mental disorders, and to examine the interactive effects of depression and chronic conditions (hypertension and diabetes) on cognitive function, life satisfaction, and episodic memory among Chinese adults aged 45 and *** Using data from the China Health and Retirement Longitudinal Study (CHARLS), 13,930 participants aged 45 and above were analyzed across five waves from 2011 to 2020. Depression was measured with the CES-D-10 scale, and participants were grouped based on symptom severity. Logistic regression models were used to examine disease risks, while linear regression models assessed the interactive effects on cognitive *** Logistic regression models showed that higher levels of depression were significantly associated with increased risk of heart disease, while showing more complex relationships with stroke and mental illness risks. Subgroup analysis using linear regression indicated that coexisting conditions like hypertension and diabetes intensified the negative impact of depression on cognitive abilities, life satisfaction, and episodic *** These findings underscore the necessity of integrating mental health management into rehabilitation strategies for patients with chronic illnesses. The study suggests that addressing mental health, particularly among high-risk older adult populations with chronic diseases, may improve recovery outcomes, reduce rehabilitation duration, and enhance quality of life based on the observed associations. This highlights the importance of comprehensive care that combines mental and physical health interventions to optimize rehabilitation outcomes.

关键词： Security   Blockchains   Federated learning   Faces   Data models   Real-time systems   Computer science   Computer architecture   Computational modeling   Servers   Blockchain   federated learning   Internet of Vehicles   certificateless threshold signature   identifiable abort  

摘要： As a promising subdomain of intelligent transportation systems (ITS), Internet of Vehicles (IoV) can be empowered by digital twin (DT) technology for real-time traffic simulation and artificial intelligence (AI)-driven predictive analytics in evolutionary trend projection, demonstrating significant potential in dynamic transportation optimization. Among various machine learning paradigms, federated learning (FL) not only aligns well with IoV, but also provides it with privacy protection. Traditional FL faces single point of failure due to the existence of an aggregation center, so blockchain-based FL with multiple aggregators is utilized to mitigate this issue. Nevertheless, in such distributed environments, both aggregators and model parameters exposed to network are vulnerable to attacks, impeding the normal operation of FL. In this paper, for blockchain-enabled FL with multiple aggregators in IoV, we propose CLTSwNI&IA, the first non-interactive certificateless threshold signature with identifiable abort. This scheme eliminates certificate management and key escrow, adopts a blockchain-oriented approach by utilizing a fully distributed signing paradigm. Additionally, the proposed signing scheme is capable of identifying malicious FL aggregators during the entire process through distributed fine-grained verification and ensuring the integrity of aggregation results. Finally, theoretical and experimental comparisons with related literature demonstrate the advanced functionality and the acceptable efficiency of our approach.

关键词： Applied sciences   Computer science   Artificial intelligence  

摘要： Level 3 automated driving systems require drivers to occasionally retake control, making it crucial to understand factors influencing takeover performance. This study investigates how single versus multiple non-driving-related tasks (NDRTs) affect driver behavior, perceptions, and takeover capability. Using a high-fidelity driving simulator with 32 participants, conditions varying NDRT types and task-switching frequency were examined. Results indicate that diverse NDRT engagement and frequent task switching significantly influence takeover performance. Moreover, our decision tree models highlighted the number of NDRT types and task-switching frequency as critical predictors of driver response effectiveness. These findings underscore the importance of accounting for task engagement diversity and switching behaviors in automated vehicle system design. This study provides practical implications for optimizing human-automation interactions and enhancing the safety of automated driving systems by managing drivers' attentiveness for their in-cabin activities.

关键词： Microbiology   Computer science  

摘要： The accurate computational annotation of protein sequences with enzymatic function remains a fundamental challenge in bioinformatics. Here, we present HiFi-NN (Hierarchically-Finetuned Nearest Neighbor search) which annotates protein sequences to the 4th level of Enzyme Commission (EC) number with greater precision and recall than state-of-the-art deep learning methods. Furthermore, we show that this method can correctly identify the EC number of a given sequence to lower identities than BLASTp. We show that performance can be improved by increasing the diversity of the lookup set in both sequence space and the environment the sequence has been sampled from. We proceed to show that we can correct specific mis-annotations in the BRENDA enzymes database reproducing results found by others. Finally, we use HiFi-NN to annotate functional dark-matter protein sequences from NMPFamDB. Our findings pave the way for more accurate functional annotation in silico, especially for proteins from distant sequence space.

关键词： Computer science   Electrical engineering   Energy engineering  

摘要： To address the limitations of current transmission line point cloud segmentation algorithms in accurately segmenting fine-grained structures, this paper proposes a model named TLSNet. The network addresses the issue of uneven point cloud distribution through a dynamic density adaptation mechanism (DALNF-Layer), achieves cross-scale context modeling with a hierarchical-offset transformer (DLCTransformer), and optimizes gradient propagation using the inverted residual module (InvResMLP). Together, these components form an efficient point cloud segmentation framework tailored for the complex scenarios of transmission lines. Experimental results demonstrate that TLSNet achieves precise segmentation of key regions in transmission line point clouds, outperforming existing algorithms in segmentation accuracy. This advancement provides a solid technical foundation for the digital operation and maintenance of transmission lines, as well as for autonomous unmanned aerial vehicle (UAV) inspection path planning.

关键词： Physics   Computer science   Engineering  

摘要： Spiking neural networks (SNNs) offer a biologically inspired alternative to artificial neural networks (ANNs) by mimicking neuronal information transmission mechanisms. However, similar to ANNs, SNNs remain susceptible to adversarial examples, raising concerns about their robustness in practical applications. To address this vulnerability, we propose the Random Heterogeneous Spiking Neural Network (RandHet-SNN), inspired by the heterogeneity and stochasticity observed in biological neural systems. This architecture strengthens the network's defense against adversarial attacks by introducing neuron-level diversity through randomized time decay constants, allowing each neuron to acquire unique temporal properties at every forward pass. We evaluate RandHet-SNN's performance through extensive experiments with various adversarial attacks. Results indicate that RandHet-SNN significantly enhances network robustness while maintaining minimal impact on clean accuracy. RandHet-SNN displays significant potential for robust, energy-efficient neural computing in adversarial environments.