Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an image-to-image translation paradigm, treating multiple modalities as input channels. However, these approaches often yield sub-optimal results due to the inherent difficulty in achieving precise feature- or semantic-level alignment across modalities.
This data set includes student responses and expert ratings from the test administrations for the Turkish History course. For each question, the correct answer is assigned a new label, while incorrect answers are labeled as “0”. For 15 questions, there are true-false scores and for 4 questions there are true-partially true-false scores.
The dataset consists of airport specific ground crew and allocation data for four major airports - Kempegowda International Airport (BLR), Rajiv Gandhi International Airport (HYD), Indira Gandhi International Airport (DEL), and Chhatrapati Shivaji Maharaj International Airport (BOM). The tasks, floors and gates, i.e, the tasks and their locations are factual data where as the allocation data is approximately close to realistic demand. The crew demand is synthetically generated.
The Cross-Domain Deception Dataset (CD3) contains frame-level features extracted from video data using OpenFace and OpenPose to support research in deception detection through facial expressions, facial action units, body and hand gestures, and gaze coordinates. Using a commercial off-the-shelf laptop and Microsoft Teams, we collected video data of 45 participants completing mock interviews where they answered questions related to biographical information, academic success, and well-being across two sessions.
6 typical suppression-type interference signals are generated by MATLAB 2021b: Sine amplitude modulation(SAM), Sine frequency modulation(SFM), Noise frequency modulation(NFM), Noise amplitude modulation(NAM), Linear frequency modulation sweep(LFM), Logarithmic frequency modulation sweep (LogFM).
This study aims to create a robust hand grasp recognition system using surface electromyography (sEMG) data collected from four electrodes. The grasps to be utilized in this study include cylindrical grasp, spherical grasp, tripod grasp, lateral grasp, hook grasp, and pinch grasp. The proposed system seeks to address common challenges, such as electrode shift, inter-day difference, and individual difference, which have historically hindered the practicality and accuracy of sEMG-based systems.
The dataset utilized in this study corresponds to the year 2019 (all months) and covers an extensive region within the Mediterranean Basin. This particular year was selected to investigate seasonal variations in column-averaged dry-air mole fraction of methane (XCH₄), providing insights into methane dynamics over different climatic periods.
The series of file names starting with "01-CuIndex" are the raw data. "datasets122" refers to the processed dataset, and "test_datasets" is the test dataset.
The scarcity of multimodal datasets in remote sensing, particularly those combining high-resolution imagery with descriptive textual annotations, limits advancements in context-aware analysis. To address this, we introduce a novel dataset comprising 12,473 aerial and satellite images sourced from established benchmarks (RSSCN7, DLRSD, iSAID, LoveDA, and WHU), enriched with automatically generated pseudo-captions and semantic tags.
The GestDoor dataset contains wearable sensor data collected to support research in biometric authentication through arm movements during door-opening interactions. Using two 6-degree-of-freedom (6-DOF) inertial measurement units (IMUs) worn on the wrist and upper arm, 11 participants performed four types of door-opening tasks—left-hand pull, left-hand push, right-hand pull, and right-hand push—across up to three sessions. The dataset includes 3,330 samples comprising accelerometer and gyroscope signals at 100 Hz, along with session metadata.
