The Special things such as the pandemic are changing people's work and life style, and there additionally has been a remarkable change in the inflow and outflow rules of bike sharing, whether it is the tidal law in time, or the gathering points in space. These dynamic changes bring new challenges to the bicycle scheduling. Based on the flow characteristics of bike sharing during the pandemic, this paper presents a solution for dynamic scheduling of bike sharing: combining K-means and K-medoids algorithm to solve isolated site scheduling problems like nucleic acid detection stations; establishing a scheduling model based on fixed cost and transportation cost between stations; and finding the best scheduling path based on the positive correlation between distance cost and the mileage of dispatching vehicles. The scheme is conducted on the actual operation data of bike sharing and distribution of nucleic acid detection station in a certain area, and the results show that the scheme is feasible and effective.
Attention mechanisms have been found to be effective for human gaze estimation. To address the problem that traditional attention has limited ability to extract higher-order contextual information in gaze estimation tasks, an ECA attention mechanism-based gaze estimation network is proposed, which aims to effectively exploit the channel relations of features through a global average pooling layer without dimensionality reduction, suppress some facial regions that do not contribute to gaze estimation, and activate subtle facial features that can improve gaze estimation. The model can take full advantage of the user's appearance, which helps to improve the accuracy of the gaze estimation model. In this paper, experiments are conducted on the MPIIGaze dataset, and the results show that the network based on the channel attention mechanism can reduce the estimation error, and the model proposed in this paper can achieve more accurate gaze estimation.
A person's state is reflected in many aspects, such as emotions and body movements. Online teaching makes it difficult for teachers to accurately understand the learning status of students due to the separation of space between teachers and students. This paper extracts images from video cameras, from which identifies the learner's emotion, head posture and fatigue, and evaluates the learner's learning state by synthesizing the three-sided information. The seven emotions were divided into three categories: negative, positive and natural. Head posture is defined by Euler angles, and fatigue is determined by blinking frequency. Hierarchical decision-making method is used in the model for information fusion. The learning state assessment method proposed in this paper integrates the performance of both internal and external aspects of psychology and behavior, and has high reliability. Real-time understanding of students' learning status can help improve the effectiveness of teaching.
Depression is one of the most common mental illnesses in the world today. Unlike anxiety in daily life, depression is often accompanied by prolonged low mood, slow thinking, unresponsiveness and difficulty in self-regulation. In severe cases, it can affect life and even lead to death. In this paper, a multimodal depression classification model based on BiGRU and BiLSTM is proposed in the publicly available Chinese dataset EATD-Corpus. Audio and text features are extracted using the vggish model and elmo respectively. The features are not fused. After the audio and text features are trained separately for detection, BiGRU and BiLSTM are adaptively weighted and fused to detect depression. The method has a precision value of 0.66, an F1-score value of 0.77 and a recall value of 0.97. The experimental results show that the performance of the method has been improved.
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