Machine Learning
Moving away from plain-text DNS communications,
users now have the option of using encrypted DNS protocols
for domain name resolutions. DNS-over-QUIC (DoQ) employs
QUIC—the latest transport protocol—for encrypted communi-
cations between users and their recursive DNS servers. QUIC is
also poised to become the foundation of our daily web browsing
experience by replacing TCP with HTTPP/3, the latest version
of the HTTP protocol.
Traditional TCP-based web browsing is vulnerable to website
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This dataset is collected at KAIST, Daejeon, and KAIST by ISILAB to research seamless indoor-outdoor detection. The collecting device is a Raspberry Pi 4B+ with touchscreen UI connected with a Pmod Nav module and a PmodGPS. This collection has a rough three-month time span, which mitigates the specific time-specific bias. Further, in the collection, we also swap the wiring to simulate the device bias. The dynamic calibration is not applied to the dataset; searchers may choose to apply the dataset or not.
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This dataset integrates three Publicly available sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
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This dataset integrates three valuable sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
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The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
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The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
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Create Graph：we collected asymmetric drug-drug interaction (DDI) entries from version 5.1.12 of DrugBank, released on March 14, 2024. After a thorough double-check, we removed drugs with incorrect SMILES strings or those that could not be represented by Morgan fingerprints . This filtering resulted in a dataset containing 1,752 drugs and 508,512 asymmetric interactions. Subsequently, we organized the DDI entries into a directed interaction network, where directed edges represent the asymmetric interactions between drugs.
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Training and testing the accuracy of machine learning or deep learning based on cybersecurity applications requires gathering and analyzing various sources of data including the Internet of Things (IoT), especially Industrial IoT (IIoT). Minimizing high-dimensional spaces and choosing significant features and assessments from various data sources remain significant challenges in the investigation of those data sources. The research study introduces an innovative IIoT system dataset called UKMNCT_IIoT_FDIA, that gathered network, operating system, and telemetry data.
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Accurate and spatiotemporal seamless soil moisture (SM) products are important for hydrological drought monitoring and agricultural water management. Currently, physically-based process models with data assimilation are widely used for global seamless SM generation, such as soil moisture active passive level 4 (SMAP L4), the land component of the fifth generation of European Reanalysis (ERA5-land) and Global Land Data Assimilation System Noah (GLDAS-Noah).
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The increasing number of wildfires damages nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection.
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