*.avi; *.csv; *.txt

We construct the triple-modality dataset, VGG-sound+, comprising image-text-audio data. Based on VGG-sound, VGG-sound+ consists of 200,000 audio-visual data entries categorized as video data, including metadata label- ing the category of each video clip. We define the image-text-audio triplet modalities of VGG-sound+ as the dataset Di = (Ii , Ti , Ai), where Ii represents an image snap- shot of the video, Ti denotes a textual description of the video, and Ai signifies the audio clip.

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644 Views

The Protection of Children from Sexual Offences (POCSO) Act was an important legislation that was enacted in India in 2012. It aims to safeguard children from sexual exploitation through various enforcement and legal redressal mechanisms. This dataset has been scraped from eCourts India Services using Python script which uses Selenium. We have mined apex and high courts’ judgements, which mentioned the POCSO Act and its respective sections. We have chronologically scraped POCSO judgements from 2012 to 2020 in the corpus.

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382 Views

Relative direction estimation among neighboring vehicles in urban environments is essential to a wide variety

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263 Views

Pressure sensors based on fiber Bragg gratings in side-hole optical fiber enable remote monitoring of pressure at multiple points within many otherwise inaccessible environments. However, sensors fabricated from side-hole fiber drawn from a preform have limitations in their design and material composition. The design of such sensors is a compromise between achieving good sensitivity, while also minimizing splice losses due to mode-mismatch.

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44 Views

The dataset is built upon a multi-layer multi-domain (MLMD) network topology of a real testbed.

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335 Views

This is a collection of scripts to perform essential preprocessing steps,

doing denoise and feature extraction of EEG (ERP) data using MATLAB and the EEGLAB toolbox.

The scripts are under current development with no guarantee of proper

functioning. The scripts are published in the hopes of helping people with interests in our experiment to reproduce the steps under their environment, and maybe extend to further improvement in the future. Currently, this project works with Muse S band (cortex TP9, TP10, AF7, AF8) device and is implemented with XDF raw Data. 

 

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Latent fingerprint identification is crucial in forensic science for linking suspects to crime scenes. Latent examiners obtain unique, reliable evidence by revealing hidden prints through advanced techniques. However, latent fingerprints often are partial prints with undesirable characteristics such as noise or distortion. Due to these characteristics, identifying the physical details of a latent fingerprint, known as minutiae, is a complex task. Recent publications found that there are subsets on one minutia in latent fingerprints that, when removed, increase the matching score.

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123 Views

Dataset from UCI contains the  electricity consumption (in kWh) of 320 customers every 15 minutes from 2011 to 2014.     For  some customers, their electricity consumption data was recorded starting from a different time,  and the data before the first record was marked as 0.     As training model requires a starting point for the time series, and different customers  have different starting times, some features remain 0 before a certain time point.     Using all  the data for training would inevitably have a negative impact.     Therefore, the data  from 2011 is removed  and set the starti

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We use a total of 16 datasets, detailed descriptions of which are provided in Table II. Among them, 11 datasets are from the UCI database, the DLBCL-Harvard dataset is from the ELVIRA biomedical database, Yale and ORL

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68 Views

This dataset was used for the capacity planing of ahybrid solar PV-small hydro power plant located in Buseruka, Hoima District in Uganda. The data consitss of four files namely:

 

1. 20240601_GenerationStatisticsQ1_2024-Kabalega-Hydromax: Represents power generation statistics from ERA for Kabalega/HYdromax HPP.

2. Hydrology from Buseruka HPP - 2015-2022: Represents the hydrology or river flow data in cumecs for the period 2015 to 2022 as provided by the plant operators.

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