Machine Learning
This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather.
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364 Views
This dataset contains a Magazine_Subscriptions.csv and a Gift_Card.csv, both of which contains a column of item id, a column of user(reviewer) id, a column of user rating, and a column of reviewed time. This data is originally collected by Ni, Jianmo, Jiacheng Li, and Julian McAuley in “Justifying Recommendations Using Distantly-Labeled Reviews and Fine-Grained Aspects,” proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
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The dataset used was derived from a frequency tunable high power vacuum electron devices (HPVED) optimization task using a simple genetic algorithm (SGA). This device has the topology with 44 optimization parameters and 2 tuning parameters. Each of the two tuning parameters has 6 and 7 discrete adjustment values, respectively. The population size is set to 54, and each individual requires 42 simulations to evaluate its tunability.
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397 Views
Crowdfunding campaigns frequently fail to reach their funding goals, posing a significant challenge for project creators. To address this issue and empower future crowdfunding stakeholders, accurate prediction models are essential. This study evaluates the relative significance of diverse modalities (visual, audio, and text) in predicting campaign success.
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224 Views
This dataset contains precomputed MS-COCO and Flickr30K Faster R-CNN image features, which are all the data needed for reproducing the experiments in "Stacked Cross Attention for Image-Text Matching", our ECCV 2018 paper. We use splits produced by Andrej Karpathy.
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56 Views
Mulan , a sourceforge net multi-target dataset available in www.openml.org. Despite the numerous interesting applications of MTR, there are only few publicly available datasets of this kind - perhaps because most applications are industrial - and most experimental evaluations of MTR methods are based on a limited amount of datasets. For this study, much effort was made for the composition of a large and diverse collection of benchmark MTR datasets.
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363 Views
Ulos is one of the traditional fabrics originating from North Sumatra, Indonesia. Ulos has various motifs and distinctive colors, which reflect the culture and philosophy of the Batak people. In this study, we collected a dataset of color ulos images with high pixel quality. The purpose of this research is to analyze the visual characteristics of ulos, such as pattern, color, and explore the potential utilization of this dataset for various applications, such as cultural preservation, product development, and visual analysis.
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144 Views
We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.
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631 Views
This study used a benchmark dataset, applying different embedding like LASER and FastText to capture contextual information, which was combined to create a new hybrid embedding. This hybrid embedding was fed to machine-learning (ML) and deep learning (DL) classifiers.
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281 Views
As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches.
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211 Views