Artificial Intelligence
The Internet Graphs (IGraphs) dataset is a substantial collection of real intra-AS (Autonomous System) graphs sourced from the Internet Topology Data Kit (ITDK) project. Comprising a total of 90,326 graphs, each ranging from 12 to 250 nodes, this dataset provides a diverse and extensive resource for the exploration and analysis of network structures within autonomous systems.
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We looked at 10,269 users on Twitter and collected their tweets and the follower network from April 2019 to October 2019. We organized tweets with the same hashtag into 29,192 cascades. To find an active community, we first selected 500 popular seed users. Subsequently, we added users who followed these seed users to the target group. After adding more users iteratively for five rounds, we locked the target group.
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This dataset has been specially curated to demonstrate various image anonymization techniques for protecting privacy-sensitive areas. Gaussian blurring is used to selectively blur parts of the image, creating a blend of the original and blurred visuals. The pixelation process reduces the image to small pixel blocks and then resizes them back to their original dimensions, creating a pixelated appearance in designated areas. Distortion is implemented through elastic deformation, adding displacement fields generated by combining a Gaussian filter with a uniform random field to the image.
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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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