the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physica...
ISBN:
(纸本)9798400708879
the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physical workers;an interface design methodology for serving machinelearning models;lions out of bounds? reflections on digital technology and matristic design to address human-wildlife conflict;an integration model to enhance information systems administration and data sharing – a case of Namibian lower courts;unmasking trust: examining users’ perspectives of facial recognition systems in Mozambique;and investigating the efficacy of large language models in reflective assessment methods through chain of thoughts prompting.
Detecting and diagnosing dermatological diseases is of paramount importance in ensuring early and accurate medical interventions, thus improving patient outcomes. In this context, our study presents a pioneering appro...
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Cybercrimes have risen steadily since the Covid-19 epidemic. Although efforts are being made to detect cyberattacks, more scientific research is still needed as cybercriminals are constantly innovating and becoming mo...
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machinelearning is widely used in the bank risk warning system more and more popular. It has been able to provide early internal bank risk warning nowadays. However, its ability of external risk warning is limited an...
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the security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection s...
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Active learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation...
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ISBN:
(纸本)9783031457241;9783031457258
Active learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the full Semantic-KITTI dataset. We extend our study over our existing work [14] only on 1/4th of the same dataset. Addition of DA and BALD have a negative impact over the labeling efficiency and thus the capacity to distill datasets. We demonstrate key issues in designing a functional AL framework and finally conclude with a review of challenges in real world active learning.
this study adopts a novel strategy using federated learning with a Convolutional Neural Network (CNN) to identify and categorize Cucurbit leaf diseases. the research includes information from six customers, each with ...
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In the personnel management knowledge graph, the absence of relations will lead to an incomplete knowledge graph, which affects downstream applications. While existing approaches can handle relational reasoning tasks,...
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A wide variety of open-source libraries and platforms support the creation of machinelearning models that provide ready-to-use implementations of many popular algorithms available under various licenses. Such softwar...
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machinelearning is used now a days in multidiscipline. Vision based or image-based application is very popular in healthcare and human interaction. the proposed model of this article is focus on visually challenged i...
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