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Titlebook: International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018; Applications and Tec Jemal Abawajy,Ki

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發(fā)表于 2025-3-23 12:26:43 | 只看該作者
12#
發(fā)表于 2025-3-23 15:29:53 | 只看該作者
Zhihao Zheng,Pu Wangnd die Werkzeugkosten deutlich geringer als beim Verfahren nach Busch. Bei gegebener Druckringkraft traten mit den gr??eren Abstreckwinkeln Rei?er auf, woraus der gleiche Einflu? des Abstreckwinkels auf die Verfahrensgrenze wie beim konventionellen Abstreckgleitziehen /65/ abzuleiten ist.
13#
發(fā)表于 2025-3-23 20:32:28 | 只看該作者
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發(fā)表于 2025-3-23 22:25:08 | 只看該作者
An Efficient Authentication Protocol for RFID-Based Supply Chainsntication named StepAuth in which the readers don’t need to preload any valid paths before authentication. StepAuth reduces the risks of leaking valid paths. In this paper, we propose a new tag path authentication protocol for RFID-based supply chain, which further improves the efficiency of StepAut
15#
發(fā)表于 2025-3-24 05:40:07 | 只看該作者
A Transfer Learning Approach for Credit Scoring auxiliary training data transfer so that the efficiency of external credit information using minority samples can be improved. With a new sample initial weight allocation and adjustment strategy, the ability to identify negative samples is highlighted. Through dynamic adjustments to auxiliary train
16#
發(fā)表于 2025-3-24 09:37:32 | 只看該作者
A Parallel Programming Model Research Based on Heterogeneous Multi-core Embedded Processorhe CPU-FPGA heterogeneous system which can be used as cluster cloud computing node. Meanwhile, we make a comparison of our model with other references and verify the efficient of the programming model in this paper.
17#
發(fā)表于 2025-3-24 12:48:32 | 只看該作者
A Gaussian-Based Trust Model for Nodes of Sensor Networks automatic credibility state and the credibility monitoring across the Internet can be realized. From the simulation analysis, it can be judged that GBTM has higher accuracy and dynamic adaptability compared with other known trust models.
18#
發(fā)表于 2025-3-24 18:51:12 | 只看該作者
19#
發(fā)表于 2025-3-24 21:30:00 | 只看該作者
Exploring Influential Factors in Recommending Algorithms for All Items and Niche Items, the graph-based model and the content-based model. We compare the algorithms commented before on Top-N recommendation tasks and evaluate their performance on recommending long-tail items. Experimental results support the effect of factor analysis for various tasks.
20#
發(fā)表于 2025-3-25 03:05:36 | 只看該作者
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