Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding *** the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to th...
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Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding *** the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining *** at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this *** this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long *** short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been *** the basis of the short patterns satisfying specific conditions,the long patterns are extracted through *** reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of *** algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational *** datasets and synthetic datasets are adopted in experiments to evaluate the proposed *** experimental results show the obvious advantages in both time efficiency and space efficiency of GTK.
Classifying large-scale data is a challenging task in machine learning. Feature selection and feature construction can improve the classification performances of classifiers. However, existing feature selections strug...
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The World Wide Web (Web) is a crucial part of the Internet. Web attacks are becoming more and more serious and complex. Malicious Web request detection aims to rapidly and accurately identify abnormal attacks on the n...
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Deep learning based person re-identification (reid) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnera...
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Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obs...
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The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as lo...
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With fast convergence and high accuracy, zeroing neural dynamics (ZND) is widely used in control applications, particularly in robotic trajectory tracking. This paper focuses on the trajectory tracking of wheeled unma...
With fast convergence and high accuracy, zeroing neural dynamics (ZND) is widely used in control applications, particularly in robotic trajectory tracking. This paper focuses on the trajectory tracking of wheeled unmanned ground vehicles (WUGVs) and presents a novel adaptive zeroing neural dynamics-based cascaded control scheme (AZNDCCS). The scheme first develops an adaptive ZND (AZND) to overcome the limitations of traditional ZNDs with fixed parameters and to mitigate the unnecessary resource consumption associated with ZNDs employing varying parameters. The trajectory tracking is then decoupled into a two-step cascaded control process, which sequentially outputs the yaw rate and velocity. The AZNDCCS is ultimately achieved by integrating a single-layer AZND into the design of the yaw rate controller and a multi-layer AZND into the design of the velocity controller. Under the AZNDCCS, once the yaw angle error converges, the WUGVs reach the desired position under the velocity controller. This cascaded design further optimizes the control performance and improves the reliability of the system. As a validation, theorems about the stability and fixed-time convergence for trajectory tracking of WUGVs under the AZNDCCS are provided. Furthermore, based on the design process of AZNDCCS, two control schemes for comparison are presented: the fixed-parameter ZND-based cascaded control scheme and the varying-parameter ZND-based cascaded control scheme. Simulation results show that WUGVs under AZNDCCS achieve faster tracking speeds or lower resource consumption than the comparative schemes.
Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-gr...
Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.
Unmanned aerial vehicles (UAV) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or ...
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Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces grea...
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