Artificial Intelligence

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.

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.

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.

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.

Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the channel state information. Current channel estimation neural networks are frequently tested by training and testing on one example channel or similar channels. However, data-driven methods often degrade on new data which they are not trained on, because they cannot extrapolate their training knowledge.

In this paper, a lightweight optimization method for complex analog integrated circuits (ICs) is proposed based on convolution neural network (CNN)-multilayer perceptron (MLP) and particle swarm optimization (PSO) algorithm. According to the circuit structure and the proposed design specifications, the circuit is divided into several sub-module circuits. Then, the sub-module and overall dataset are constructed.

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These datasets originate from two separate power transformers, identified as Transformer 1 and Transformer 2. Each dataset is further categorized into two distinct time intervals for data collection. The first time interval, labeled as "m," represents a short-term sampling period with data recorded every 15 minutes, capturing detailed temporal fluctuations over shorter durations. The second time interval, labeled as "h," signifies a longer-term sampling period with data recorded at hourly intervals, providing an overview of broader trends and patterns.

The Human voice Natural Language from On-demand media (HENLO) dataset is a high-quality emotional speech dataset created to address the need for representative and realistic data in speech emotion recognition research. Unlike many existing datasets, which rely on simulated emotions performed by untrained speakers or directed participants, HENLO sources its data from professionally produced films and podcasts available on Media On-Demand (MOD).