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This dataset is a curated and processed version of the ISIC2019 skin lesion dataset, specifically prepared for research on lightweight skin disease classification and knowledge distillation. The dataset includes:

A subset of dermoscopic images from ISIC2019, formatted and resized for training and evaluation.

Corresponding metadata tables containing patient information (e.g., age, sex, lesion location).

Pre-processed CSV files that map image names to diagnostic labels.

Split files (train/val/test) for reproducibility.

In the attached dataset, several continuous records of chirp-sequence FMCW radar bursts are presented. The data consist of the direct ADC output of an AWR1443BOOST radar board from Texas Instruments. In most of the measurements, the FMCW radar was interfered by a PMCW one operating in the same frequency band. Different bandwidths have been considered, as well as different phase codes for the interfering radar. All measurements were conducted inside an anechoic chamber, with some static items and two moving targets consisting of a flying drone and a walking pedestrian.

When artificial intelligence (AI) systems take actions or make decisions, the issue of accountability for the outcomes or decisions made by AI systems comes into question. The rapid advancement of AI technology poses a greater challenge, as current legal and ethical frameworks struggle to keep up with innovation, resulting in a lack of standardization in existing policies that comprehensively address accountability in AI.

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Ultra Wideband (UWB) signals offer high spatio-temporal resolution, penetrability, and low cost, which facilitates accurate characterization of limb features through micro-Doppler (mD) analysis, even with micro random body movements, during the wireless contactless sensing. Due to challenges introduced by arm motions, which may be perpendicular to the radar, we introduce a dual radar arm motion recognition system with light-weighted feature extraction and appropriate data fusion.

Ultra Wideband (UWB) signals offer high spatio-temporal resolution, penetrability, and low cost, which facilitates accurate characterization of limb features through micro-Doppler (mD) analysis, even with micro random body movements, during the wireless contactless sensing. Due to challenges introduced by arm motions, which may be perpendicular to the radar, we introduce a dual radar arm motion recognition system with light-weighted feature extraction and appropriate data fusion.

This dataset is a unified compilation of the Electricity Transformer Temperature (ETT) datasets: ETTh1, ETTh2, ETTm1, and ETTm2. It includes both hourly and minute-level temperature and load data collected from power transformers, which are vital for developing and benchmarking time-series forecasting models. The dataset contains features such as high and medium voltage transformer temperatures (HUFL, HULL, MUFL, MULL) and the operational temperature (OT), which serves as the primary prediction target.

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

To support research on multimodal speech emotion recognition (SER), we developed a dual-channel emotional speech database featuring synchronized recordings of bone-conducted (BC) and air-conducted (AC) speech. The recordings were conducted in a professionally treated anechoic chamber with 100 gender-balanced volunteers. AC speech was captured via a digital microphone on the left channel, while BC speech was recorded from an in-ear BC microphone on the right channel, both at a 44.1 kHz sampling rate to ensure high-fidelity audio. 

The LibriSpeech corpus, a publicly available English speech dataset derived from audiobook recordings. The corpus contains approximately 1,000 hours of 16 kHz read speech from over 2,400 speakers, encompassing diverse speaking styles, rates, and regional accents. For the purpose of contrastive learning, a subset of 100 speakers was sampled, with 20 utterances per speaker ranging from 3 to 10 seconds. The dataset provides clean, labeled speech suitable for tasks involving speaker representation, acoustic modeling, and multi-style synthesis.

This study utilizes the open-source datasets FAIR1M and HRSC2016 as foundational resources to construct an optical remote sensing image dataset for rotated ship target detection. The dataset encompasses nine ship categories: Dry-Cargo-Ship, Engineering-Ship, Fishing-Boat, Motorboat, Tugboat, Passenger-Ship, Warship, Liquid-Cargo-Ship, and Other-Ship.

We created two high-quality point label segmentation datasets, as shown on the left side of Figure 1, WD with a camera magnification of 3 and PLD with a magnification of 40, respectively. The WD is a focal magnification of 3 times, which is a larger field of view consisting of thinner wires, which may be power lines, fiber optic cables, steel wire pulling cables, etc. The PLD is a magnification to a maximum focal length of 40 times, which consists of high-voltage power lines or fiber optic cables with distinctive features.

This is a voiceprint dataset with speaker's normal voice and special voice.

We recruited user test participants by snowballing. We directly invited 34 participants, all of them are family members, friends and classmates of the authors, ranging in age from 16 to 55, with similar technical backgrounds. Next, the involved participants further invited their relatives and friends to join, then 21 additional participants joined our user study. Finally, 56 people participated in our user study.

This is a voiceprint dataset with speaker's normal voice and special voice.

We recruited user test participants by snowballing. We directly invited 34 participants, all of them are family members, friends and classmates of the authors, ranging in age from 16 to 55, with similar technical backgrounds. Next, the involved participants further invited their relatives and friends to join, then 21 additional participants joined our user study. Finally, 56 people participated in our user study.

This file contains the source codes of the proposed end-nodes for a Wireless Sensor Network (WSN) for hydrometeorological monitoring in the article entitled "Hydrometeorological Monitoring using Wireless Sensor Networks". These codes were developed to perform LoRaWAN communication range tests and to test two distinct sensor nodes with different functionalities: a meteorological sensor node and a hydrological sensor node.

The Lemon Leaf Disease Dataset (LLDD) is a high-quality image dataset designed for training and evaluating machine learning models for lemon leaf disease classification. The dataset contains 9  classes of images of healthy and diseased lemon leaves, such as; Anthracnose. Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites, making it suitable for tasks such as plant disease instance segmentation, detection, image classification, and deep learning applications in agriculture.

These folders contain images showcasing various aspects of orange fruit and  leaf diseases, including black spot, greening, scap, canker diseases, melanose, and healthy leaves. The dataset serves as a valuable resource for research, machine learning model training, and analysis in the field of citrus diseases and nutrient imbalances.

The data covers the period from January 4, 2021, to August 16, 2023. It includes the carbon trading prices from the Hubei carbon market and other relevant feature data that may influence carbon prices. The feature data has undergone preliminary screening and consists of Brent crude oil prices, natural gas prices, Rotterdam coal prices, EU Emission Allowances, the China Securities 300 Index, and the Euro exchange rate.

This dataset was built as part of our study MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control. The dataset was sourced from mental health-related posts in Reddit Mental Health Dataset and tagged with responses from mental health professionals to selected posts. For more details on building the dataset, please see the paper.

The sampling rate of laser Doppler vibration measurement data is 250MHZ, and the number of sampling points is 20M.A total of 12 frequency bands of 0.5kHZ, 1kHZ, 2kHZ, 3kHZ, 4kHZ, 5kHZ, 6kHZ, 7kHZ, 8kHZ, 9kHZ, 10kHZ, 11kHZ were collected, which ensured that there were at least 5 vibration samples in each frequency band, and 20 samples were collected in some frequency bands.