Artificial Intelligence

AI-powered binary code similarity detection (BinSD), which transforms intricate binary code comparison to the distance measure of code embedding through neural networks, has been widely applied to program analysis. However, due to the diversity of the adopted embedding strategies, evaluation methodologies, running environments, and/or benchmarks, it is difficult to quantitatively understand to what extent the BinSD problem has been solved, especially in real-world applications.

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As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.

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222 Views

As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.

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128 Views

The data set has been prepared as 2 different versions. The data set was shared in two versions due to the fact that the researchers could easily reproduce the tests and hardware limitations. The first version (small_dataset) was prepared using a 10% sub-sample of all dataset. The other version (big_dataset) contains the entire data. In this study, the scenarios tested were run on the small_dataset. The most successful configuration that was selected as a result of the analysis on small_dataset was applied to big_dataset.

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Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022.

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574 Views

We collected relevant data of ultrasonic Doppler flowmeter in the laboratory to study the error of ultrasonic Doppler flowmeter. It contains four sets of data at different turbidities and four sets of data at different liquid levels. Each set of data under different turbidities contains 440 pieces of data, and each set of experiments under different liquid levels contains 220 pieces of data. The entire data set has a total of 2720 pieces of data. The training set test split is 8:2, which we have already split in the uploaded data set.

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Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach.

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754 Views

Using this data, we conduct an extensive investigation into the phenomenon of homophily in the generation of hate speech on Twitter, shedding light on an essential aspect of understanding online hate speech dynamics. We introduce innovative methods to detect multiple forms of hate speech, including manifestations of racism and sexism. Furthermore, we propose and validate novel measures for quantifying familiarity and similarity on Twitter, providing a comprehensive framework for understanding the interactions among users.

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74 Views

The "Multi-modal Sentiment Analysis Dataset for Urdu Language Opinion Videos" is a valuable resource aimed at advancing research in sentiment analysis, natural language processing, and multimedia content understanding. This dataset is specifically curated to cater to the unique context of Urdu language opinion videos, a dynamic and influential content category in the digital landscape.

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