Artificial Intelligence
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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This dataset is designed to advance research in Visual Question Answering (VQA), specifically addressing challenges related to language priors and compositional reasoning. It incorporates question labels categorizing queries based on their susceptibility to either issue, allowing for targeted evaluation of VQA models. The dataset consists of 33,051 training images and 14,165 validation images, along with 571,244 training questions and 245,087 validation questions. Among the training questions, 313,664 focus on compositional reasoning, while 257,580 pertain to language prior.
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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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The PermGuard dataset is a carefully crafted Android Malware dataset that maps Android permissions to exploitation techniques, providing valuable insights into how malware can exploit these permissions. It consists of 55,911 benign and 55,911 malware apps, creating a balanced dataset for analysis. APK files were sourced from AndroZoo, including applications scanned between January 1, 2019, and July 1, 2024. A novel construction method extracts Android permissions and links them to exploitation techniques, enabling a deeper understanding of permission misuse.
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The SINEW 15 2023 Biomarker dataset was extracted from the sensor data collected by a longitudinal study called Sensors IN-home for Elder Wellbeing (SINEW).
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In the captured image, a drone is seen in flight, displaying its advanced technological features and capabilities. The image highlights the drone's robust design and aerodynamic structure, which are essential for its diverse applications in research and development. Drones, also known as Unmanned Aerial Vehicles (UAVs), are increasingly being utilized in various fields due to their ability to collect data from hard-to-reach or hazardous areas.
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Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. However, despite advancements, there remains a lack of standardized benchmarks for evaluating MLLMs performance in multi-object sentiment analysis, a key task in semantic understanding. To address this gap, we introduce MOSABench, a novel evaluation dataset designed specifically for multi-object sentiment analysis.
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