Artificial Intelligence
This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023.
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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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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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Optical Coherence Tomography (OCT) is a non-invasive imaging technology widely used in endoscopic examinations. Saturation artifacts occur when the intensity of the light signal received by the detector exceeds its dynamic range, causing image distortion. This distortion can manifest as excessive brightness or blurriness in specific areas of the image, thereby affecting the quality of the imaging.
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Image inpainting is a great challenge when reconstructed with realistic textures and required to enhance the consistency of semantic structures in large-scale missing regions. However, popular structural-prior guided methods rely mainly on the structural features, which directly accumulate and propagate random noise, causing inconsistencies in contextual semantics within the flled regions and poor network robustness.
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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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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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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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The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.
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