Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

In situ establishment of Co/MoS_(2) heterostructures onto inverse opal‐structured N,S‐doped carbon hollow nanospheres:Interfacial and architectural dual engineering for efficient hydrogen evolution reaction

Rational design of low‐cost and high‐efficiency non‐precious metal‐based catalysts toward the hydrogen evolution reaction(HER)is of paramount significance for the sustainable development of the hydrogen economy.Interfacial manipulationinduced electronic modulation represents a sophisticated strategy to enhance the intrinsic activity of a non‐precious electrocatalyst.Herein,we demonstrate the straightforward construction of Co/MoS_(2) hetero‐nanoparticles anchored on inverse opal‐structured N,S‐doped carbon hollow nanospheres with an ordered macroporous framework(denoted as Co/MoS_(2)@N,S-CHNSs hereafter)via a templateassisted method.Systematic experimental evidence and theoretical calculations reveal that the formation of Co/MoS_(2) heterojunctions can effectively modulate the electronic configuration of active sites and optimize the reaction pathways,remarkably boosting the intrinsic activity.Moreover,the inverse opal‐structured carbon substrate with an ordered porous framework is favorable to enlarge the accessible surface area and provide multidimensional mass transport channels,dramatically expediting the reaction kinetics.Thanks to the compositional synergy and structural superiority,the fabricated Co/MoS_(2)@N,S-CHNSs exhibit excellent HER activity with a low overpotential of 105mV to afford a current density of 10 mA/cm^(2).The rationale of interface manipulation and architectural design herein is anticipated to be inspirable for the future development of efficient and earth‐abundant electrocatalysts for a variety of energy conversion systems.