Machine Learning
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27 Views
Object tracking systems within closed environments employ light detection and ranging (LiDAR) to address privacy and confidentiality. Data collection occurred in two distinct scenarios. The goal of scenario one is to detect the locations of multiple objects from various locations on a flat surface in a closed environment. The second scenario describes the effectiveness of the technique in detecting multiple objects by using LiDAR data obtained from a single, fixed location.
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369 Views
Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.
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693 Views
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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1523 Views
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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14 Views
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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30 Views
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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523 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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198 Views