Structure Learning Research Articles

Prioritizing Indicators for Rapid Response in Global Health Security: A Bayesian Network Approach

This study explored a Bayesian belief networks (BBNs) approach, developing two distinct models for prioritizing the seven indicators related to the “rapid response to and mitigation of the spread of an epidemic” category within the context of both the specific category and the Global Health Security Index (GHS index). Utilizing data from the 2021 GHS index, the methodology involves rigorous preprocessing, the application of the augmented naive Bayes algorithm for structural learning, and k-fold cross-validation. Key findings show unique perspectives in both BBN models. In the mutual value of information analysis, “linking public health and security authorities” emerged as the key predictor for the “rapid response to and mitigation of the spread of an epidemic” category, while “emergency preparedness and response planning” assumed precedence for the GHS index. Sensitivity analysis highlighted the critical role of “emergency preparedness and response planning” and “linking public health and security authorities” in extreme performance states, with “access to communications infrastructure” and “trade and travel restrictions” exhibiting varied significance. The BBN models exhibit high predictive accuracy, achieving 83.3% and 82.3% accuracy for extreme states in “rapid response to and mitigation of the spread of an epidemic” and the GHS index, respectively. This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

Integrating feedback mechanisms and ChatGPT for VR-based experiential learning: impacts on reflective thinking and AIoT physical hands-on tasks

ABSTRACT This study investigates the application of Virtual Reality (VR) in the educational field, particularly its integration with GAI technologies such as ChatGPT to enhance the learning experience. The research indicates that while VR provides an immersive learning environment fostering student interaction and interest, the lack of a structured learning framework and personalized feedback may limit its educational effectiveness and potentially affect the transfer of VR-learned knowledge to physical hands-on tasks. Hence, it calls for the provision of more targeted and personalized feedback in VR learning environments. Through a randomized controlled trial (RCT), this study collected data from 77 university students, integrating experiential learning in VR for acquiring AIoT knowledge and practical skills, and compared the effects of traditional feedback versus GPT feedback on promoting reflective thinking, learning motivation, cognitive levels, and AIoT hands-on abilities among the students. The results show that the group receiving GPT feedback significantly outperformed the control group across these learning indicators, demonstrating the effectiveness of GAI technologies in providing personalized learning support, facilitating deep learning, and enhancing educational outcomes. This study offers new insights into the integration of GAI technology in VR learning environments, paving new pathways for the development and application of future educational technologies.

Optimizing human SIRT6 protein with deep learning of 3D structures based on maximum lifespan

Optimizing human SIRT6 protein with deep learning of 3D structures based on maximum lifespan

Learning Style Preferences of BSEd- Math Major Students in Mathematics of Investment

Student learning styles should affect assessment and instruction. Adapting the idea that people process information differently should guide our teaching. Much research has focused on learning style variations. Many feel that certain learning styles are better for certain subjects or expertise. This research examines students' learning styles and academic performance in Mathematics of Investment. VAK learning styles preference model correlation with BSEd Math majors' academic achievement. All first-year Visayas State University Bachelor of Secondary Education majors in Mathematics were targeted. The non-random purposive sampling method selected 27 students as respondents. Data was acquired using descriptive-survey research and Cynthia Arem's Learning Style Preference Inventory questionnaire. The Chi-Square Test compared Students' arithmetic grades to their learning styles. Results show that learning style affects student academic performance. Education-style questionnaires can help educators structure learning events in ways that are more popular or familiar to students by revealing respondents' preferences or self- beliefs. Educators should not assume that such attempts would improve student learning except to motivate volunteer effort. Knowing students' preferred learning styles can help educators overcome their tendency to treat all pupils equally and inspire them to use others.

Improved brain effective connectivity modelling by dynamic Bayesian networks

Background:If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. New method:This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. Results:The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. Comparison with existing methods:The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. Conclusions:Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

SGK-Net: A Novel Navigation Scene Graph Generation Network.

Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation.

A Scalable Accelerator for Local Score Computation of Structure Learning in Bayesian Networks

A Bayesian network is a powerful tool for representing uncertainty in data, offering transparent and interpretable inference, unlike neural networks’ black-box mechanisms. To fully harness the potential of Bayesian networks, it is essential to learn the graph structure that appropriately represents variable interrelations within data. Score-based structure learning, which involves constructing collections of potentially optimal parent sets for each variable, is computationally intensive, especially when dealing with high-dimensional data in discrete random variables. Our proposed novel acceleration algorithm extracts high levels of parallelism, offering significant advantages even with reduced reusability of computational results. In addition, it employs an elastic data representation tailored for parallel computation, making it FPGA-friendly and optimizing module occupancy while ensuring uniform handling of diverse problem scenarios. Demonstrated on a Xilinx Alveo U50 FPGA, our implementation significantly outperforms optimal CPU algorithms and is several times faster than GPU implementations on an NVIDIA TITAN RTX. Furthermore, the results of performance modeling for the accelerator indicate that, for sufficiently large problem instances, it is weakly scalable, meaning that it effectively utilizes increased computational resources for parallelization. To our knowledge, this is the first study to propose a comprehensive methodology for accelerating score-based structure learning, blending algorithmic and architectural considerations.

Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering

Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of view-specific information for fusion representation, affecting the learning of discriminative cluster structure. In this paper, we propose a new framework of Progressive Neighbor-masked Contrastive Learning for FDMC (PNCL-FDMC) to tackle the aforementioned issues. Specifically, by using neighbor-masked contrastive learning, PNCL-FDMC can explicitly maintain the local structure during the embedding aggregation, which is beneficial to the common semantics enhancement on the fusion view. Based on the consistent aggregation, the fusion view is further enhanced by diversity-aware cluster structure enhancement. In this process, the enhanced cluster assignments and cluster discrepancies are employed to guide the weighted neighbor-masked contrastive alignment of semantic structure between individual views and the fusion view. Extensive experiments validate the effectiveness of the proposed framework, revealing its ability in discriminative representation learning and improving clustering performance.

Self-supervised reconstructed graph learning for link prediction in bipartite graphs

Graph Neural Network(GNN) has achieved remarkable performance in classification tasks due to its strong distinctive power of different graph topologies. However, traditional GNNs face great limitations in link prediction tasks since they learn vertex embeddings from fixed input graphs thus the learned embeddings cannot reflect unobserved graph structures. Graph-learning based GNNs have shown better performance by collaborative learning of graph structures and vertex embeddings, but most of them rely on available initial features to refine graphs and almost perform graph learning at once. Recently, some methods utilizes contrastive learning to facilitate link prediction, but their graph augmentation strategies are predefined only on original graphs, and do not introduce unobserved edges into augmented graphs. To this end, a self-supervised reconstructed graph learning (SRGL) method is proposed. The key points of SRGL lie in two folds: Firstly, it generates augmented graphs for contrasting by learning reconstructed graphs and vertex embeddings from each other, which brings unobserved edges into augmented graphs. Secondly, it maximizes the mutual information between edge-level embeddings of reconstructed graphs and the graph-level embedding of an original graph, which guarantees learned reconstructed graphs to be relevant to the original graph.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

Unsupervised gas pipeline network leakage detection method based on improved graph deviation network

Historical data has shown that the natural gas pipeline network, as a critical urban lifeline system, is susceptible to disasters such as earthquakes resulting in leakage. Graph neural network modeling provides frontier rapid detection solutions for pipeline leakage; however, the challenges of collecting gas network anomaly data for training limit the precision and robustness of current model. This research proposes an unsupervised gas leakage detection and localization method based on a contained preprocessing process, with a graph deviation model that combines a structural learning approach with a graph neural network to model the spatial dependence of the sensors based on an attention mechanism, and variational inference models the posterior distributions of the hyper-parameters to optimize the model and improve the model precision. Meanwhile, in the preprocessing stage, the automatic optimization strategy of Northern Goshawk Optimization (NGO) for the best parameters K and ɑ in the Variable Mode Decomposition (VMD) efficiently extracts the valid signals and ensures that the model gives full play to the detection and localization effectiveness. The gas pipeline network dataset constructed by Pipeline Studio was used for comparing the performance of the model in this study with other frontier models. The results demonstrate that the model in this research has competitive detection Precision (93.31%), Recall (70.82%), and F1-score (0.81), and the posterior distribution of the model parameters strengthens the gas leakage localization precision, which provides a comprehensive solution for subsequent decision-making and reduces the hidden hazard of leakage effectively.

Joint structure learning and causal effect estimation for categorical graphical models.

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.

Efektivitas Penerapan Sistem Informasi Manajemen di SMPS Galih Agung Deli Serdang dalam Peningkatan Kualitas Pendidikan

This study aims to describe and evaluate the effectiveness of the implementation of the Management Information System (MIS) at SMPS Galih Agung Deli Serdang in improving the quality of education. The research employs a descriptive qualitative method with a case study approach, collecting data through interviews, observations, and documentation studies. The results indicate that the implementation of the school website and the Academic Information System (Dapodik) has enhanced information accessibility and management efficiency. The school website provides a real-time platform for students, parents, and teachers to access important information, while Dapodik speeds up academic administrative processes such as recording student attendance and grades. The implementation of these systems has facilitated information access, improved administrative efficiency, and positively impacted lesson planning and parental involvement in education. The study also identified challenges in resource management and monitoring, such as a lack of guidelines for activity planning and an imbalance in staff workload. The training provided has improved staff understanding of planning, and the redistribution of teaching duties has reduced workload-related complaints. The implications of this research suggest that effective management information system implementation can enhance educational quality by facilitating information access, improving administrative efficiency, and supporting a more structured learning process.

Consensus representation-driven structured graph learning for multi-view clustering

Consensus representation-driven structured graph learning for multi-view clustering

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. In addition, an error compensation mechanism that uses an error node to capture the temporal fluctuation caused by system degradation or failures is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The results show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92 %–95 % from 1 h to one week ahead forecasting.

A Hybrid Method: Resolving the Impact of Variable Ordering in Bayesian Network Structure Learning

A Hybrid Method: Resolving the Impact of Variable Ordering in Bayesian Network Structure Learning

ADVANTAGES AND DISADVANTAGES OF STUDENTS’ GROUP WORK IN TEACHING FOREIGN LANGUAGES AT UNIVERSITY

The aim of the article is analysing the advantages and disadvantages of group work for students while teaching foreign languages at university, showing the feasibility of using group work in higher edu- cation. Methods of analysis and systematization of primary sources, interviews, questionnaires, and focus groups were used during the consideration of the problem. The process of teaching English in higher education has certain features that are associated with dif- ferent initial levels of language training of freshmen; the number of hours to study the subject; the number of groups; and sometimes absent enthusiasm for learning a foreign language. We emphasize that group work plays an important role in the educational process, and its introduction into the educational process should be one of the main tasks for students of philological specialties. Practice has proved that high-quality training in foreign languages for philological specialties can be carried out through the introduction of mod- ern educational technologies, such as: vocational training, project methodology, technology, such as: voca- tional training, project methodology, technology of fixing and distance learning, information and commu- nication technologies, and educational and control work. The study found that the use of interactive learn- ing technology involves seeking help in communication, including cognitive communication and constructiv- ist methods of teaching a foreign language. The facts proved that the use of innovative methods of teach- ing English and multimedia teaching methods can enhance the motivation of students to study foreign lan- guages, provide access to new alternative sources of information, develop independent psychological activ- ity, and develop communication skills, intercultural and professional abilities. Interactive technologies and online resources play an important role in group work while learning for- eign languages, providing an effective and engaging learning process. They promote active interaction be- tween students, stimulate involvement in language learning and improve language skills. To determine the advantages and disadvantages of group work, we conducted interviews with teach- ers and students to collect their impressions and thoughts about group work in the context of teaching for- eign languages. Subsequently, the reviews of teachers and students about their experience in groups dur- ing the study of foreign languages were analysed. Questionnaires were compiled, which included questions about impressions and thoughts about group work in the context of studying foreign languages. The questions concerned the advantages and dis- advantages of this training method, the efficiency from group work, the influence on motivation, etc. Fo- cus groups were held with students and separately with teachers to discuss their impressions and thoughts about group work. This allowed us to gain a deeper understanding of different points of view and identi- fy general trends. In group work during the study of foreign languages, the teacher and students play different roles, each of which has its own importance for successful learning. We will discuss the role of the teacher-facili- tator. The teacher acts as an organizer and leader of group work, promoting structured learning and prob- lem solving. Students actively interact with each other and with the teacher, exchanging ideas and opin- ions, completing tasks and solving problems. Scientists have demonstrated that the active participation of students in group work contributes to the improvement of critical thinking and the development of com- munication skills. The use of group work while teaching foreign languages at university has its advantages and disadvantages. On the one hand, it promotes the development of communication skills, and stimulates cooperation and motivation of students. On the other hand, there may be a risk of social blind steering, problems with the distribution of responsibilities and conflicts between group members. It has been concluded that it is important to properly organize and control the process of group work to achieve maximum efficiency in teaching foreign languages.

Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation

Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship

Using daily data spanning from 5 January 2004 to 22 November 2022, we quantify the spillover effects between 42 global stock markets. Specifically, combining causal structure learning and Elastic-Net-VAR methods, we innovatively construct multilayer causal networks based on volume-price relationship. Then, we analyze the network characteristics of multilayer spillover networks from system and market levels. Our findings indicate that there is heterogeneity in risk spillovers of price and volume networks, highlighting how trading volume spillover network play an important role in the risk contagion. Furthermore, multilayer interconnected networks confirm the risk spillovers between volume and price, and it exhibits significant differences compared to single-layer network. In addition, at system-level, each network layer shows unique network structures and dynamic evolution characteristics. At market-level, global stock markets play different roles in emitting or receiving shocks through various transmission channels. Our study emphasizes the importance of intra- and inter-layer risk propagation in multilayer networks based on volume and price, and has significant implications for developing investment strategies and global portfolio risk management.