Machine Learning
The UCI dataset is a data repository maintained and made available by the University of California, Irvine that is widely used for machine learning and data mining research. The dataset covers a wide range of fields and topics, including but not limited to medicine, biology, social sciences, physics, engineering, and more. The uniqueness of this dataset is that it contains data from multiple different domains and sources, allowing researchers to explore and analyze the data from different perspectives and contexts.
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This dataset contains simulation data of the LightGBM controller for robotic manipulator. The data were generated using a closed-loop system of spacecraft attitude dynamics under an exact feedback linearization-based controller.
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This dataset contains the input and output data from an industrial case study aiming to detect undesired non-intuitive behavior in an engineered complex system (an Autonomous Surface Vessel (ASV) on a Search and Rescue (SAR) mission). We used the Taguchi method to set up experiments, conducted the experiments in a case company specific test arena, and performed different forms of regression analysis.
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Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack
to the recent Poolz Finance arithmetic overflow incident. While historically, the definition of smart contract
vulnerabilities lacked standardization, even with the current advancements in Solidity smart contracts, the
potential for deploying malicious contracts to exploit legitimate ones persists.
The abstract Syntax Tree (AST ), Opcodes, and Control Flow Graph (CFG) are the intermediate representa-
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521 Views
Automatic white balance (AWB) is an important module for color constancy of cameras. The classification of the normal image and the color-distorted image is critical to realize intelligent AWB. One tenth of ImageNet is utilized as the normal image dataset for training, validating and testing. The distorted dataset is constructed by the proposed theory for generation of color distortion. To generate various distorted color, histogram shifting and matching are proposed to randomly adjust the histogram position or shape.
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252 Views
To conduct a comprehensive evaluation of MotifMDA's effectiveness, HMDD V2.0 \cite{li2014hmdd} is employed as the benchmark dataset. It encompasses 495 miRNAs, 383 diseases, and 5,430 human MDAs that have been confirmed through experimental validation. Moreover, another well-regarded database, namely miR2Disease, is also employed as a benchmark dataset in our study.
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63 Views
As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge.The deployment of IoT applications makes protection more challenging with the increased attack surfaces as well as the vulnerable and resource-constrained devices. Anomaly detection is a crucial procedure in protecting IoT. A promising way to perform anomaly detection on IoT is through the use of machine learning algorithms. There is a lack in the literature to identify the optimal (with regard to both effectiveness and efficiency) anomaly detection models for IoT.
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This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
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4385 Views
STP dataset is a dataset for Arabic text detection on traffic panels in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 506 images were gathered through manual collection one by one, with each image energizing Arabic text detection challenges in natural scene images according to real existing complexity of 15 different routes in addition to ring roads, roundabouts, intersections, airport and highways.
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222 Views
Climate change has been a worldwide concern for more than 50 years now and climate change misinformation has also been a critical issue as it questions the causes and effects of climate change, hence disturbing climate action. Climate misinformation has been a major obstacle to mitigating climate change and its effects, and it even aggravated the issue and polarized the public. In this paper, we introduce a new climate change misinformation and stance detection dataset namely ClimateMiSt, consisting of both social media data and news article data with manually verified labels.
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