Machine Learning
ETFP (Eye-Tracking and Fixation Points) consists of two eye-tracking datasets: EToCVD (Eye-Tracking of Colour Vision Deficiencies) and ETTO (Eye-Tracking Through Objects). The former is a collection of images, their corresponding eye-movement coordinates and the fixation point maps, obtained by involving two cohorts, respectively, people with and without CVD (Colour Vision Deficiencies). The latter collects images with just one object laying on a homogeneous background, the corresponding eye-movement coordinates and fixation point maps gathered during eye-tracking sessions.
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We have developed this dataset for the Bangla image caption. Here, we have recorded 500 images with one caption of each. Basically the lifestyle, festivals are mainly focused in this dataset. We have accomplished rice/harvest festivals, snake charming, palanquin, merry-go-round, slum, blacksmith, potter, fisherman, tat shilpo, jamdani, shutki chash, date juice, hal chash, tokai, pohela falgun, gaye holud, etc.
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The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model.
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LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measruement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1200 objects and classified it into 4 groups of object namely, human, cars, motorcyclist.
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This dataset was used in our work "See-through a Vehicle: Augmenting Road Safety Information using Visual Perception and Camera Communication in Vehicles" published in the IEEE Transactions on Vehicular Technology (TVT). In this work, we present the design, implementation and evaluation of non-line-of-sight (NLOS) perception to achieve a virtual see-through functionality for road vehicles.
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Silk fibroin is the structural fiber of the silk filament and it is usually separated from the external fibroin by a chemical process called degumming. This process consists in an alkali bath in which the silk cocoons are boiled for a determined time. It is also known that the degumming process impacts the property of the outcoming silk fibroin fibers.
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Feature tables and source code for Camargo et al. A Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors. Transactions on Biomedical Engineering. 2021
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Automatic humor detection has interesting use cases in modern technologies, such as chatbots and virtual assistants. Existing humor detection datasets usually combined formal non-humorous texts and informal jokes with incompatible statistics (text length, words count, etc.). This makes it more likely to detect humor with simple analytical models and without understanding the underlying latent lingual features and structures.
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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)
"Object classification from randomized EEG trials"
If you use this code or data, please cite the above paper.
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Dataset used in the article "On the shape of timing distributions in free text keystroke dynamics profiles". Contains CSV files with the timing features (hold times and flight times) of every keypress in three free text datasets used in previous studies, by the author (LSIA) and two other unrelated groups (KM from and PROSODY, subdivided in GAY, GUN, and REVIEW). The timing features are grouped by dataset, user, task, virtual key code, and feature. Two different languages are represented, Spanish in LSIA and English in KM and PROSODY.
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