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Distribution-based Clustering
This video covers the distribution-based clustering, which is based on the statistical distribution models. Objects belonging to the same...
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Blockchain-Based Public Distribution System
The Indian government manages the Public Distribution System (PDS) to provide the ration among the poorest sections of the society. Currently, this...
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Quantum Key Distribution
A new class of computers, so-called quantum computers, will soon be able to crack common encryption algorithms. Quantum key distribution is a...
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Sales and Distribution
The primary responsibility in sales and distribution is to provide customers with goods and services. In order to perform this task, the sales order...
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A Distribution Network Reconfiguration Method Considering Optimal Distribution of DG Output
Distribution Network Reconfiguration (DNR) is an important means to reduce line loss and improve power supply reliability. It is an important...
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Content Distribution for Mobile Internet: A Cloud-based Approach
Content distribution, i.e., distributing digital content from one node to another node or multiple nodes, is the most fundamental function of the...
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Improved Joint Distribution Adaptation for Fault Diagnosis
In the blast furnace (BF) ironmaking process, it is difficult to obtain labeled fault samples and the probability distribution drifts significantly....
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Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous...
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Distribution of Motifs in Mongolian Word Length
Word length is a crucial metric in text quantification. Investigations into Mongolian word length motifs have profound implications for broadening...
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pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints
Statistical heterogeneity, especially feature distribution skewness, among the distributed data is a common phenomenon in practice, which is a...
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A new three-parameter lifetime distribution for environmental data analysis: the Harris extended modified Lindley distribution
Statistical modeling data is crucial for identifying patterns, correlations, and trends that can be used to make informed decisions and put...
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Vitunix: A Lightweight and Secure Linux Distribution
Vitunix is a lightweight Linux distribution designed to be fast, secure, and highly customizable. Unlike traditional Linux distributions that are...
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Distribution-Adaptive Graph Attention Networks for Flood Forecasting
Flood forecasting is an important task for disaster prevention and mitigation. Many recent researchers intend to utilize data-driven deep-learning...
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Pairwise-Emotion Data Distribution Smoothing for Emotion Recognition
In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is...
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Noise-Robust Gaussian Distribution Based Imbalanced Oversampling
Imbalanced data classification has become one of the hot topics in the field of data mining and machine learning. Oversampling is one of the...
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Distribution Grid Monitoring Based on Widely Available Smart Plugs
The growing popularity of e-mobility, heat pumps, and renewable generation such as photovoltaics is leading to scenarios which the distribution grid...
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Enhancing GAN Compression by Image Probability Distribution Distillation
This paper presents a novel approach named Image Probability Distribution Distillation (IPDD) for compressing generative adversarial networks (GANs)...
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Sharpness-Aware Minimization for Out-of-Distribution Generalization
Machine learning models often suffer from a significant decline in performance when they encounter out-of-distribution (OOD) data that differs from...
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Quantum Key Distribution in Internet of Things
This chapter aims to explore the integration of Quantum Key Distribution (QKD) with IoT systems. It delves into the potential benefits, challenges,...
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Rethinking Distribution Alignment for Inter-class Fairness
Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning....