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关键词： Latches   Integrated circuit reliability   Design automation   Reliability engineering   Inverters   Delays   Computer science   Transistors   Simulation   Radiation hardening (electronics)   Algorithm-based verification   latch design   source-drain cross-coupled inverters (SCIs)   triple node upset (TNU)  

摘要： With the rapid advancement of semiconductor technologies, latches become increasingly sensitive to soft errors, especially triple node upsets (TNUs), in harsh radiation environments. In this article, we first propose a high-performance and area-efficient latch, namely, HALTRAV, featuring complete TNU-recovery. The storage portion of HALTRAV consists of 28 interlocked source-drain cross-coupled inverters (SCIs) for complete TNU-recovery with area efficiency and low delay. To mitigate the issue that node-upset-recovery verifications for existing latches highly relies on electronic design automation tools, we further propose an algorithm-based verification method that can automatically verify the node-upset-recovery of latches, which greatly simplifies the reliability-verification flow. Simulation results demonstrate the TNU-recovery of HALTRAV and also show that HALTRAV achieves 40.38%, 8.17%, and 31.89% reduction in delay, area, and delay-power-area product (DPAP) on average, respectively;however;it is at the cost of power as compared to typical latches that are TNU-recoverable. Comparison results also demonstrate the moderate sensitivity of HALTRAV to the impacts of the process, voltage, and temperature (PVT) variations.

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： Computer Science   Data Imputation   Data Mining   Interdisciplinary Applications   Performance Evaluation of Imputation Methods  

摘要： In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. Most of the literature in this field deals with different imputation methods. However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations. The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models. We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV;these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods. The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files. This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files. The results show that -globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means. An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori.

关键词： Heuristic algorithms   Probabilistic logic   Servers   Crowdsensing   Electronic mail   Mirrors   Computer science   Channel allocation   Optimization   Dynamic scheduling   Probabilistic maximum coverage   online learning   combinatorial bandit   network application optimization  

摘要： The Probabilistic Maximum Coverage (PMC) problem plays a pivotal role in modeling various network applications, such as mobile crowdsensing, which involves selecting nodes within a graph that probabilistically cover other nodes. Our study focuses on PMC within the framework of online learning, termed the PMC bandit, where the network parameters are initially unknown. In this scenario, the decision-maker is tasked with learning these parameters to maximize the cumulative rewards from covered nodes. Despite prior research on the PMC bandit, we propose a novel variant, dynamic PMC-G bandit, which extends the semi-bandit feedback model to represent applications more accurately. To tackle the complexities of the time-varying combinatorial arm set rather than traditional static, we enhance the Combinatorial Upper Confidence Bound (CUCB) algorithms by developing two innovative variance-aware strategies: the Variance-Adaptive Combinatorial Upper Confidence Bound (VACUCB) for probabilistically triggered arms, and the Action-Based Combinatorial Upper Confidence Bound (ABCUCB) for self-reliant arms, i.e., independent arms with probabilistically triggered outcomes. Based on variance-aware properties, our contributions notably reduce the dependence on the number of nodes K selected per round, demonstrating that: (i) VACUCB effectively minimizes the regret associated with the triggered arms, enhancing the CUCB by a factor of (O) over tilde (K;(ii) ABCUCB further diminishes the dependence on K in the leading term. Empirical results from synthetic and real-world datasets confirm that our proposed algorithms outperform current benchmarks in three network applications.

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.