Machine Learning
Memes (photos with text) for fine-tuning AGI. Training in recognizing and generating memes, jokes, mockery, trolling, emotions in foreign languages. This is a demo sample (will be expanded) before data expansion. Hundreds of variants were created for each meme. Meme text without using the expanded data set is difficult enough for AGI to correctly recognize emotions. With the data set expansion, the accuracy increases significantly.
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This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1.
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RSHIP137 is a self-built remote sensing dataset of ships, consisting of 119,330 images across 137 categories. The size of each image varies, with the largest having dimensions of 182x699 and the smallest being 7x11. The distribution of categories is highly imbalanced, with the most frequent category being "Barge," which contains 31,466 images, and the least frequent category being "901-fast combat support ship," with only 15 images.
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Mirror arrays have been applied to indoor visible light communication (VLC) as a passive the reconfigurable intelligent surface (RIS) which has no signal processing to solve the problem of indoor visible light line-of-sight obstruction, however, after channel modeling, it is found that the reflected channel in this scenario has a serious multipath effect, to this end, we introduce deep learning techniques into channel estimation of VLC systems with mirror arrays for the first time, and propose a hybrid new model of Transformer and the bidirectional longshort-term memory model (Transformer-B
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13 Views
This is a pump fillage time series data set, consisting of 8 time series. The data is sourced from actual production data during the operational process of an oil field. It includes data from 8 oil wells, with measurements collected every half hour between July 22, 2022, and August 16, 2022. The pump fillage is extracted from the operational process of an oil field. The pump fillage data for each well is sorted in chronological order to obtain the pump fillage time series for each well. The data set had varying numbers of cards due to potential communication issues, rangin
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4 Views
This is a pump fillage time series data set, consisting of 8 time series. The data is sourced from actual production data during the operational process of an oil field. It includes data from 8 oil wells, with measurements collected every half hour between July 22, 2022, and August 16, 2022. The pump fillage is extracted from the operational process of an oil field. The pump fillage data for each well is sorted in chronological order to obtain the pump fillage time series for each well. The data set had varying numbers of cards due to potential communication issues, rangin
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5 Views
A key challenge in cybersecurity is the absence of a large-scale network dataset that accurately captures modern traffic patterns, diverse intrusion types, and comprehensive network activity. Existing benchmark datasets such as KDDCup99, NSL-KDD, GureKDD, and UNSW-NB15 require updates to reflect contemporary cyberattack signatures effectively.
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70 Views
This dataset contains electromagnetic field (EMF) intensity measurements recorded at half-hour intervals. The dataset spans a continuous timeline, capturing variations in electric field strength in volts per meter (V/m). It serves as a valuable resource for environmental monitoring, predictive modeling, and studying the impacts of EMF exposure. Applications include urban planning, public health assessments, and advanced regression or machine learning modeling.
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Pacemaker use due to conduction abnormalities is a common complication following surgical aortic valve replacement (AVR). Heart rate variability (HRV) is associated with sinus node dysfunction and significant dysrhythmias. However, its predictive value for postoperative electrical pacing requirements after AVR remains unclear. This retrospective study reviewed pre-registered electrical records from 194 adult patients who underwent isolated AVR. HRV parameters in both time and frequency domains were obtained prior to anesthesia induction and before initiating cardiopulmonary bypass.
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The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.
Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.
Total Samples: 12,800
Training Set (including validation set): 10,240
Test Set: 2,560
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35 Views