Conventional classroom instruction often struggles to effectively convey cultural heritage due to constraints in spatial and temporal dimensions, limiting students' ability to fully engage with and appreciate historical content. In contrast, virtual reality (VR) technology offers a human-centered, immersive way to present cultural heritage, creating a dynamic digital experience particularly beneficial when physical access to heritage sites is unavailable. This study investigates whether VR-based learning can enhance students' performance in cultural education compared to traditional teaching methods. A sample of 228 primary school students from Grades 5 and 6 was randomly assigned to one of two groups: a high-visual engagement group (VR with 360° video) or a low-visual engagement group (static video and textbook). The findings revealed that students in the high-visual engagement group achieved higher levels of intrinsic motivation and demonstrated greater learning improvements than their counterparts in the low-visual engagement group. Furthermore, the study identified negative user experiences as a significant factor moderating the connection between intrinsic motivation and learning outcomes. These results highlight the value of integrating VR into conventional teaching practices, showcasing its potential to enhance student engagement and improve educational outcomes in history and cultural studies.

The morphological diversity, referring to the variations in traffic network topologies defined in this paper, often emerges and brings difficulties in successfully transferring a pre-trained prediction model from one traffic network to another. Moreover, most existing research primarily assumes that traffic data in source and target networks follow independent and identically distributed (i.i.d.) patterns, which is usually not consistent with real-world situations, particularly when considering morphological diversity. For this inconsistency, many efforts have been made, but they mainly concentrate on temporal aspects, which significantly differ from traffic prediction due to spatial and temporal correlations among road segments, influenced by variations in road topology and traffic behavior. This paper introduces a causality-based spatiotemporal out-of-distribution (OOD) generalization method, which is adaptable to most GNNs for diverse, large-scale, dynamic traffic systems with zero-shot. Furthermore, to enhance the generalization and adaptability of the proposed method, we introduce graph matching and equal-sized graph partitioning to alleviate spatial shift between the source and target traffic networks, reduce and align the scale of the networks. Experiments carried out on traffic flow datasets demonstrate that our method significantly improves the performance of various GNN-based traffic predictors in the situation of morphological diversity, achieving a maximum reduction in MAE of 33.08%. Compared to other OOD-driven baselines, our approach also shows a notable improvement, with up to a 40.58% decrease in MAE.

Learning analytics (LA) holds a promise to transform education by utilizing data for evidence-based decision-making. Yet, its application in early childhood education (ECE) remains relatively under-explored. ECE plays a crucial role in fostering fundamental numeracy and literacy skills. While standardized tests was intended to be used to monitor student progress, they have been increasingly assumed summative and high-stake due to the substantial impact. The pressures in succeeding in such standardized tests have been well-documented to negatively affect both students and teachers. Attempting to ease such stress and better support students and teachers, the current study delved into the LA potential for predicting standardized test performance using formative assessments. Beyond predictive accuracy, the study addressed ethical considerations related to fairness to uncover potential risks associated with LA adoption. Our findings revealed a promising opportunity to empower teachers and schools with more time and room to help students better prepared based on predictions obtained earlier before standardized tests. Notably, bias can be significantly observed in predictions for students with disabilities even they have same actual competence compared to students without disabilities. In addition, we noticed that inclusion of demographic attribute had no significant impact on the predictive accuracy, and not necessarily exacerbate the overall predictive bias, but may significantly affect the predictions received by certain demographic subgroups (e.g., students with different types of disability).

Summary An effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

Current optimization for stateful flow processing computation tends to focus on load balancing without considering the utilization of downstream instance resources. To address this issue, we propose a data stream grouping method called Fa-Stream, specifically designed for stateful operators and incorporating field values frequency-awareness. Fa-Stream is implemented in three main aspects: (1) A data stream grouping model is built using Count-Min Sketch and Gated Recurrent Unit (GRU) to predict and analyze the frequency of field values. It selectively chooses high-frequency field values, and the communication distance model and instance resource constraint model are designed to adjust the weights of downstream instances for high-frequency field values. (2) A cyclic access routing table is generated, and weights are dynamically adjusted by a rebalancing scheme to avoid load skewness. Consistent hash grouping is implemented for low-frequency field values, and dual mapping is used to prevent large-scale migration caused by scaling. To validate the effectiveness of Fa-Stream, comparative experiments between Partial Key Grouping (PKG) and Fa-Stream are conducted using the Storm platform. Results demonstrate that Fa-Stream improves tuple throughput by 12.3%, reduces system delay by 14.2%, and increases load balancing degree by 42.8%. Furthermore, fa-Stream exhibits efficiency and stability across different data skews and tuple input rates.

Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.

Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secretion pathways. The non-classical secretion pathway has recently received increasing attention among these secretion pathways, but its exact mechanism remains unclear. Non-classical secreted proteins (NCSPs) are a class of secreted proteins lacking signal peptides and motifs. Several NCSP predictors have been proposed to identify NCSPs and most of them employed the whole amino acid sequence of NCSPs to construct the model. However, the sequence length of different proteins varies greatly. In addition, not all regions of the protein are equally important and some local regions are not relevant to the secretion. The functional regions of the protein, particularly in the N- and C-terminal regions, contain important determinants for secretion. In this study, we propose a new hybrid deep learning-based framework, referred to as ASPIRER, which improves the prediction of NCSPs from amino acid sequences. More specifically, it combines a whole sequence-based XGBoost model and an N-terminal sequence-based convolutional neural network model; 5-fold cross-validation and independent tests demonstrate that ASPIRER achieves superior performance than existing state-of-the-art approaches. The source code and curated datasets of ASPIRER are publicly available at https://github.com/yanwu20/ASPIRER/. ASPIRER is anticipated to be a useful tool for improved prediction of novel putative NCSPs from sequences information and prioritization of candidate proteins for follow-up experimental validation.

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.

Abstract Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.

For medical research purposes, having access to large sets of data, often from various regions, improves statistical outcomes of analysis. However, patient data is usually considered to be sensitive and access to it is restricted by law and regulation. This paper employs privatization techniques which enable sharing of sensitive data. We demonstrate a case study on four medical data sets.