Artificial Intelligence
5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models.
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This dataset contains audio recordings and transcriptions of toxic speech derived from Indonesian conversations during YouTube videos where scammers are confronted. The dataset captures two separate interactions that escalate into toxic exchanges. Each interaction has been verified by native Indonesian speakers and labeled into two classes: toxic and non-toxic. The dataset includes both the original and preprocessed versions of the speech and text data. The original speech files total 136MB, while the preprocessed speech files are 111,7MB.
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The dataset is derived from Monte Carlo simulations, generating EV charging power curves. For training the Physics-Informed Neural Networks (PINNs), we have statistically organized the data with the x-axis representing the State of Charge (SoC) state space, the y-axis representing time, and the z-axis representing the corresponding number of electric vehicles. The z-axis data has been normalized. The uploaded data is intended for training within the PINN framework to obtain the EV aggregation model and its parameters.
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Cora, Citeseer, and Pubmed are commonly used citation network datasets. Among these, Citeseer has the most dense features, while Pubmed has more nodes and edges.
ACM is a network of papers where each node represents a paper. In contrast to citation networks, edges connect papers that share the same authors.
Flickr serves as a social network that captures the connections between users originating from image and video hosting websites. These users are categorized into nine groups according to their personal interests.
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The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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This is the video dataset for SFDM paper. Only the first 30 seconds are to be used. The last 10 seconds are extended so that the trimming of the video does not make each clip end abruptly before 30 seconds.
File naming style: {case study number}{sequence}{clip number}{opt: falsely detected as other sequence}.webm
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In order to develop and analyse the performance of large-scale colored point cloud upsampling, we built a large-scale colored point cloud dataset for training and evaluating the upsampling network. This large-scale colored point cloud dataset consists of 121 original colored point clouds, 43 of which were scanned by us, while the other 78 were obtained from the SIAT-PCQD, Moving Picture Experts Group (MPEG) point cloud, and Greyc 3D colored mesh database. These point clouds cover six categories, including animals, plants, toys, sculptures, people and others.
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Brain-Computer Interface (BCI) technology facilitates a direct connection between the brain and external devices by interpreting neural signals. It is critical to have datasets that contain patient's native languages while developing BCI-based solutions for neurological disorders. However, present BCI research lacks appropriate language-specific datasets, particularly for languages such as Telugu, which is spoken by more than 90 million people in India.
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334 Views
The dataset1 includes fake&real news propagation networks on Twitter built according to fact-check information and the news retweet graphs were originally extracted by [FakeNewsNet](https://github.com/KaiDMML/FakeNewsNet).The statistics of the dataset is shown below:| Data | #Graphs | #Total Nodes | #Total Edges | #Avg.
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Abstract
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3150 Views