<p class="MsoNormal"><span style="mso-spacerun: 'yes'; font-family: 宋体; mso-ascii-font-family: Calibri; mso-hansi-font-family: Calibri; mso-bidi-font-family: 'Times New Roman'; font-size: 10.5000pt; mso-font-kerning: 1.0000pt;"><span style="font-family: Calibri;">This dataset contains expert evaluations of various text features using Grey Relational Analysis (GRA), comparing the performance of original and new prompt words.
This dataset comprises 32-bit floating-point SAR images in TIFF format, capturing coastal regions. It includes corresponding ground truth masks that differentiate between land and water areas. The covered regions include the Netherlands, London, Ireland, Spain, France, Lisbon, the USA, India, Africa, and Italy. The SAR images were acquired in Interferometric Wide (IW) mode with dual polarization at a spatial resolution of 10m × 10m.
Emotional classification (valence) in textual data has proved to be central to human experience analysis and natural language processing (NLP). This study implements a text mining model and algorithm - TM-EV (Text Mining for Emotional Valence Analysis) - that determines the impact of emotional valence (EV) shown by undergraduate students in their feedback (n=665860) during the program (pre- and post-course to determine its relationship with the learning outcome and performance.
Dataset for QoS-aware LLM Routing Experiment.
paper abstract:
Sample dataset of a freehand 3D ultrafast Doppler scan acquired during brain tumor resection surgery. The dataset includes the beamformed ultrasound images, as well as the simultaneously acquired optical tracking data, describing the position of the ultrasound probe in space.
Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
This dataset provides a comprehensive collection of various resources, including the results from Computational Fluid Dynamics (CFD) simulations, the associated CFD processing code, and the dataset along with the source code used for training Convolutional Neural Networks (CNNs). Additionally, it includes data generated by genetic algorithms and the corresponding source code for implementing these algorithms.
This dataset is designed for the classification of textual transcriptions of spoken conversations in Shanghai dialect and Mandarin Chinese. It consists of high-quality, manually transcribed texts from natural dialogues, annotated with corresponding language labels (Shanghai dialect: 1, Mandarin: 0). The dataset aims to facilitate research in text-based dialect classification, natural language processing (NLP), and linguistic variation analysis.
Research data for Mitigating Safety Risks in Information Systems: A Self-Adaptive Approach Research Data. A controlled experiment was conducted to determine whether SAS, aware of interruptions, improves effectiveness, efficiency, situational awareness, and usability compared to non-adaptive systems in information systems. A total of 30 participants contributed to the controlled experiment.
This dataset comprises user-generated content from Stack Overflow, including post bodies, post tags, and user engagement metrics such as upvotes and downvotes. The data was collected from the stack exchange explorer based on user defined categories and other criteria like reputation and badges as explained in our work. It was collected to support research in technology and emotion analysis, focusing on understanding user interactions and sentiments within online communities.
Speckle contrast optical spectroscopy (SCOS) is an optical technique capable of measuring human cerebral blood flow and brain function non-invasively. Its tomographic extension, speckle contrast optical tomography (SCOT), can provide blood flow variation maps with measurements using overlapping source-detector channel pairs. Linearity is often assumed in most image reconstruction methods, but non-linearity could exist in the relations between measured signals and blood flow variations.
This 3DTeethSegX dataset is a benchmark dataset specifically designed for tooth point cloud completion and segmentation tasks. Built upon the publicly available 3DTeethSeg 2022 MICCAI Challenge dataset, it comprises 1,494 pairs of tooth point clouds and their corresponding tooth images from 38 patients. Each pair includes a partial point cloud (2,048 points) and a complete point cloud (16,384 points).
RetinaX dataset is built by selectively combining four publicly available datasets: the STARE dataset, ARIA dataset, RFMiD dataset, and RFMiD 2.0 dataset. It contains a total of 2,514 images and 24 distinct labels, covering nearly all common and rare retinal diseases.
Monitoring sweat rate provides valuable insights into an individual’s risk of dehydration, thermoregulation efficiency, and electrolyte balance, particularly relevant for workers in hot environments, athletes, and individuals with certain metabolic conditions. Traditional methods for measuring sweat rates, such as gravimetric techniques, are labor-intensive and unsuitable for real-time monitoring.
The growing adoption of declarative software specification languages, coupled with their inherent difficulty in debugging, has underscored the need for effective and automated repair techniques applicable to such languages. Researchers have recently explored various methods to automatically repair declarative software specifications, such as template-based repair, feedback-driven iterative repair, and bounded exhaustive approaches. The latest developments in Large Language Models (LLMs) provide new opportunities for the automatic repair of declarative specifications.
Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This paper introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information.
There are four scripts in this repository in the "code" folder.The"fft RF-AM-MLP.py","fft-MLP.py","Linear static.py" are the training and testing procedures of "RF-AM-MLP","MLP" and "static linear model" respectively. And after training all the three models are saved to remain there parameters unchanged.The "fft plot figures.py" compares the three models using different validation sets which are 30rpm,60rpm,100rpm and variable rates.And different figures are drawn to show the prediction results and error distribution of the three models.
In my datasets, the "training data" is the data for the model training,the "static" is the data where peristaltic pump speed at 10pm. And others are the validaiton data of variable rates level descent and three constant rates level descent respectively as their title says
. For every set of data, there are 3 columns. The first is time, the second is capacitance and the third is liquid level.
The overall process of UWB technology-based signal preprocessing and high-precision localization system architecture can be divided into two complementary modules: perception and localization. In “Sensing Scene”, the system firstly collects the CIR data generated in the environment through UWB sensors, and applies signal conversion and multi-dimensional feature mapping methods to analyze the signal attenuation characteristics of different NLOS environments, so as to realize the segmentation of the NLOS scene.
The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to afef.zwidi@regim.usf.tn
The TUROS-TS encompasses 5,357 Google Street View images with 8,775 traffic sign instances covering 9 categories and 28 classes. Three subsets of the dataset were created: test (10%-1050 images 579), validation (20% -1050 images), and training (70% - 3728 images). It is available upon request. If you want to train and test the data set. Please send an email to afef.zwidi@regim.usf.tn
