Machine Learning

SAR-optical remote sensing couples are widely exploited for their complementarity for land-cover and crops classifications, image registration, change detections and early warning systems. Nevertheless, most of these applications are performed on flat areas and cannot be generalized to mountainous regions. Indeed, steep slopes are disturbing the range sampling which causes strong distortions in radar acquisitions - namely, foreshortening, shadows and layovers.

In contemporary digital environments, the development of a high-resolution synthetic Latin character dataset holds paramount significance across various real-world applications within the domains of  computer vision and artificial intelligence. This relevance extends from tasks such as image restoration to the implementation of sophisticated recognition systems.

This is a data set needed in a research article. It is mainly about the field of reservoirs. There are some files in it. The data are obtained from the files. Readers can use the data they want to verify after learning about the relevant articles. Data are tested to verify the results.

The file contains the raw data, the processed data, and the code for both datasets. The data for all the moments of both the Abilene and G'EANT networks were eventually saved as two CSV files, with rows indicating the number of links and columns indicating the number of moments (each moment is a separate source file).  The code files for processing both datasets are also included. The file also gives the network topology diagram for both the datasets. Abilene backbone network has 12 router nodes, 30 internal links and 24 external links, and 144 OD flows.

This is the dataset used in the paper MTS4WaterR: Predicting Gate Operation in Open Canal Control with Multi-Task Sequential Model, consisting of 2 main parts, used to train the evaluator and the learner neural networks, respectively.

Each part contains several files:

An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.

The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.

We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.