artificial intelligence
These datasets contain the database of the local television channels in the greater manila area (GMA) in the Philippines as of 2020. There are 8 databases corresponding to 8 eight channels namely Channel4.xlsx, Channel5.xlsx, Channel7.xlsx, Channel9.xlsx, Channel11.xlsx,
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Wireless Intelligence Channel (WIC) is a realistic channel data set, which is designed for deep learning research work in MIMO as well as other applications. The WIC data set is obtained directly through commercial BSs and a device capable of data acquisition that is similar to a commercial terminal. The scene selection and parameter configuration involved in the experiment are based on the characteristics of realistic scenarios and the existing configurations in commercial BSs, without assumptions and restrictions on the ideal experimental conditions.
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This benchmark dataset accompanies an article paper titled ``Learning to Reuse Distractors to support Multiple Choice Question Generation in Education''. It contains a test of 298 educational questions covering multiple subjects & languages and a 77K multilingual pool of distractor vocabulary. The goal is for a given question to propose a list of relevant candidate distractors from the pool of distractors.
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Devanagari is a phonetic script that originated from Ancient Brahmi. It is the foundation of various Indian languages. According to data from the year 2022, the Devanagari Hindi script is spoken by over 342 million people worldwide and ranks third among the top 45 languages. There are approximately 11 vowels and 33 consonants and 10 numerals in the Devanagari script. The Devanagari script has no upper-or lower-case letters and is written from left to right.
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This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.
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The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
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Vision is important for transitions between different locomotor controllers (e.g., level-ground walking to stair ascent) by sensing the environment prior to physical interactions. Here we developed StairNet to support the development and comparison of deep learning models for visual recognition of stairs. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.
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