教学课程资源库 更多>>

关键词： COVID-19   hospitalisation   Myanmar   oral health   psychological factors   quality of life  

摘要： BackgroundThe complex association between COVID-19 and psychological factors may significantly impact oral health, including oral health-related quality of life (OHRQoL). Despite their interconnected nature, literature regarding their collective impact on OHRQoL is *** study aims to assess the independent and interactive effects of COVID-19 infection and psychological factors on *** and MethodsThe cross-sectional study included 158 participants from a health centre in Yangon city. Participants were categorised into three groups based on their COVID-19 infection history, hospitalisation and oxygen supply: 'not experienced', 'experienced, non-hospitalised' and 'experienced, hospitalised'. Validated instruments, including Depression, Anxiety and Stress Scale-21 (DASS-21) to measure emotional states and the Oral Health Impact Profile-14 (OHIP-14) to assess OHRQoL, were employed. Descriptive statistics, Mann-Whitney U test, Kruskal-Wallis test, linear regressions and interaction analysis were *** median OHIP-14 score was 7.2. Among 85 confirmed COVID-19 cases, 38.8% (n = 33) received hospitalisation and oxygen supply. Multivariable regression analysis identified 'experienced, hospitalised' (95% CI: 6.36, 11.76), difficulty falling asleep (95% CI: 2.65, 7.18), depressive symptoms (95% CI: 7.38, 12.44), anxiety symptoms (95% CI: 2.93, 7.52) and stress symptoms (95% CI: 4.55, 11.11) as predictors of OHIP-14. Interaction analysis revealed their associations varied by hospitalisation history. In non-hospitalised individuals, depressive symptoms were positively interacted with OHRQoL (95% CI: 1.34, 9.02). Significant associations were observed between 'experienced, hospitalised' x 'depressive, anxiety and stress symptoms' and OHRQoL in both univariable and multivariable *** COVID-19 infection history and psychological distress significantly affect OHRQoL in Myanmar participants. Moreover, a strong interactive asso

关键词： Security   Optimization   Accuracy   Computational modeling   Privacy   Adaptation models   Training   Resilience   Electronic mail   Computer science   Cloud computing   attack detection   key management   pair key selection   deep learning   hybrid optimization  

摘要： Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm's exploration strength and the tunicate swarm's exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management.

关键词： Generators   Electronic mail   Computer science   Graph neural networks   Feature extraction   Uncertainty   Data mining   Social networking (online)   Clustering methods   Training   Graph generator   graph learning   information bottleneck (IB)   multiview graph clustering (MGC)   variational inference  

摘要： Multiview graph clustering (MGC) methods are increasingly being studied due to the explosion of multiview data with graph structural information. The critical point of MGC is to better utilize view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose a variational graph generator for MGC (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then, a simple yet effective graph encoder in conjunction with the multiview clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of the VGMGC by analyzing the uncertainty of the inferred consensus graph with the information bottleneck (IB) principle. Extensive experiments demonstrate the superior performance of our VGMGC over state-of-the-art methods (SOTAs). The source code is publicly available at: https://***/cjpcool/VGMGC.

关键词： Student mental health   Music therapy   Information processing   Interactive systems   Intelligent Computers   Heart Rate Test  

摘要： In order to explore music therapy methods suitable for different types of students, this paper analyzes students' daily behavioral habits, studies the motivations and sources of their mental health activities, and constructs a student mental health data processing system based on network application development tools. Using .net application development tools as the main tool, a student mental health data information processing system is simulated and designed. Based on its own understanding of usage, this paper analyzes several commonly used system function modules and supplements the IoT smart medical health monitoring architecture. The results show that music therapy has a certain therapeutic effect on students' mental health. The high school students surveyed in this questionnaire meet the scope of application of music therapy. Combined with the various advantages of modern computer technology and IoT sensors, the mental health status of students is analyzed to restore their mental health level.

关键词： Computer science   Teachers   Computational thinking   Scratch   constructionism   Teachers' perceptions  

摘要： In recent years, there has been increasing interest in integrating Computational Thinking (CT) into elementary schools. However, few studies have focused on teachers' perceptions. This study aims to examine teachers' perceptions to bridge the gap between theory and practice. The study analysed teachers' perceptions of integrating CT into school subjects through the constructionist approach, using Scratch a leading programming platform inspired by Resnick's Lifelong Kindergarten Constructionist model. Over a school year, 31 teachers participated in a professional development program and applied this model in their classrooms. The teachers were interviewed to capture their perceptions of learning and teaching using a constructionist method. The findings revealed that teachers with creative arts experience fully embraced the model, while others partially adopted it. Teachers recognized the benefits of constructionist teaching in fostering creativity, motivation, and engagement among students. However, they struggled with balancing these benefits with school demands and constraints. Challenges included managing time effectively within constraints of school and ensuring curriculum alignment. Despite this, many teachers found that the constructionist approach positively impacted students' learning engagement and personal development. By addressing the identified challenges, teacher education designers, curriculum developers, and schools can develop practices for closing the gap between theory and practice.

关键词： Community college transfer   PhD aspirations   Graduate school pathways   Staged innovation design  

摘要： Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students' access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career theory, this manuscript draws on survey data from over 200 community college transfer students and utilizes a staged innovation design to examine a new intervention designed to pique transfer students' interests in PhD study. Findings suggest that brief targeted interventions can significantly predict transfer students' perceptions about PhD study, but that more sustained efforts will likely be necessary to influence transfer students' more tangible degree plans. In addition to highlighting implications for future research, we identify strategies for faculty and staff seeking to support community college transfer students and build access to graduate training.

关键词： Autoencoders   Tensors   Encoding   Clustering methods   Decoding   Training   Electronic mail   Computer science   Visualization   Representation learning   Graph representation learning   multiview clustering   tensor learning  

摘要： Over the past few years, multiview attributed graph clustering has achieved promising performance via various data augmentation strategies. However, we observe that the aggregation of node information in multilayer graph autoencoder (GAE) is prone to deviation, especially when edges or node attributes are randomly perturbed. To this end, we innovatively propose a tensor-representation-based multiview attributed graph clustering framework with smooth structure (MV_AGC) to avoid the bias caused by random view construction. Specifically, we first design a novel tensor-product-based high-order graph attention network (GAT) with structural constraints to realize efficient attribute fusion and semantic consistency encoding. By imposing attribute augmentation mechanisms and smooth constraints (SCs) on the proposed high-order graph attention autoencoder simultaneously, MV_AGC effectively eliminates the instability of reconstructed graph structures and learns a more compact node representation during training. In addition, we also theoretically analyze the stronger generality and expressiveness of the proposed tensor-product-based attention mechanism over the classical GAT and establish an intuitive connection between them. Furthermore, to address the performance degradation caused by clustering distribution updating, we further develop a simple yet effective clustering objective function-guided self-optimizing module for the final clustering performance improvement. Experimental results on the six benchmark datasets have demonstrated that our proposed method can achieve state-of-the-art clustering performance.

关键词： Skeleton   Planning   Robots   Approximation algorithms   Reliability   Probabilistic logic   Heuristic algorithms   Computer science   Topology   Three-dimensional displays   Motion planning   path planning   computational geometry  

摘要： We present a hierarchical RRT-based motion planning strategy, Hierarchical Annotated-Skeleton Guided RRT (HAS-RRT), guided by a workspace skeleton, to solve motion planning problems. HAS-RRT provides up to a 91% runtime reduction and builds a tree at least 30% smaller than competitors while still finding competitive-cost paths. This is because our strategy prioritizes paths indicated by the workspace guidance to efficiently find a valid motion plan for the robot. Existing methods either rely too heavily on workspace guidance or have difficulty finding narrow passages. By taking advantage of the assumptions that the workspace skeleton provides, HAS-RRT is able to build a smaller tree and find a path faster than its competitors. Additionally, we show that HAS-RRT is robust to the quality of workspace guidance provided and that, in a worst-case scenario where the workspace skeleton provides no additional insight, our method performs comparably to an unguided method.

关键词： Indexes   Blockchains   Mathematical models   Memory   Location awareness   Knowledge engineering   Data engineering   Computer science   Trustless services   Query processing   Blockchain query   index management   dynamic workloads   index benefit   verification  

摘要： Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets.

关键词： Multiplexing   Recommender systems   Graph neural networks   Correlation   Contrastive learning   Representation learning   Electronic commerce   Bipartite graph   Training   Computer science   Recommender system   graph representation learning   multiplex graph neural network   behavior pattern   relation chain  

摘要： Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10 respectively. The source code of our paper is available at https://***/lx970414/TKDE-DCMGNN.