Artificial Intelligence
The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., happiness, anger and surprise) or abstract dimensions (valence, arousal, etc.), while neglecting the fruitful nature of emotion statements.
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Human activity recognition (HAR) has attracted much attention. However, the existing HARs have shortcomings, such as few recognized activities, no identification, privacy leakage, and battery maintenance. Aiming at these shortcomings, this team has devised a body RFID skeleton that fully senses human activity and further proposes highly-accurate and fine-grained (total of 21 activities) HARs. The body RFID skeleton senses human activity by collecting tag response records of the skeleton node.
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Supplementary material for article "Learn to rotate: Part orientation for reducing support volume via generalizable reinforcement learning"
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Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes.
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190 ViewsPelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes.
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146 Views
StrcutSeg2019 provides two annotated GTV CT datasets of lung and nasopharynx cancer from Zhejiang Cancer Hospital. Each dataset contains 50 CT scans, each of which was annotated by one experienced oncologist and validated by another.
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487 Views
The increased risk to the safety of excavator personnel and difficulty in training them, combined with a manpower shortage, have led to an increased demand for machine automation. This study applies a long short-term memory algorithm for automating a bucket-tip trajectory planning AI system.
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The dataset Provides S-parameter measurements of two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under different spatial configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt as indicated in the image. The dataset can be used for training of neural networks controlling adaptive impedance matching networks.
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231 Views
Based on sea surface temperature (SST) and sea level anomaly (SLA) data, the eddy-front associations are reasonably classified into three categories according to their topological structure, which are weak association, medium association and strong association. The eddy-front association recognition network (EFARN) is used to obtain the recognized fronts, the mask of eddy-front categories and three types of eddy-front associations.
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The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.
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681 Views