Abstract: A vehicle running system based on rail transport, including a track, a framework, two levitation devices and a running device. The track is provided with a slot, in which the framework is provided. The levitation devices are provided between the track and the framework, and a top surface of the levitation device is fixedly connected to a bottom surface of the framework through a suspension structure. The running device includes two conductor plates and two running structures. The conductor plates are fixedly arranged on the inner wall of the slot, and are corresponding to the running structures. The reluctance force of magnetic wheels is converted into a driving force to drive maglev vehicles, and the levitation force is converted into a guiding force to realize the self-stabilization of vehicle guidance. A vehicle running method based on this system is also provided.

Abstract: The present disclosure provides a posture detection device and a necklace, which relates to a field of automatic control, the posture detection device is worn on a body of a user, and the posture detection device includes a sensing device, a processor, and a reminding device. The sensing device is connected to the processor, and the sensing device obtains information of back posture changes of the body of the user and sends the information to the processor. The processor is connected to the reminding device, the processor processes the information, generates a reminding signal, and sends the reminding signal to the reminding device, the reminding device starts a vibration mode according to the reminding signal.

Abstract: In some embodiments, a method selects a sequence of programs watched by a user account. The method calculates a first set of weights based on comparing content of the sequence of programs to content of a target program and calculates a second set of weights based on an order of the sequence of the programs and the first of weights. The first set of weights and the second set of weights are applied to the sequence of programs to generate a prediction of a similarity of the sequence of programs to the target program. Then, the method outputs the prediction of the similarity for use in determining a recommendation for the user account.

Abstract: In some embodiments, a method selects a sequence of programs watched by a user account. The method calculates a first set of weights based on comparing content of the sequence of programs to content of a target program and calculates a second set of weights based on an order of the sequence of the programs and the first of weights. The first set of weights and the second set of weights are applied to the sequence of programs to generate a prediction of a similarity of the sequence of programs to the target program. Then, the method outputs the prediction of the similarity for use in determining a recommendation for the user account.

Abstract: In some embodiments, a first detector generates a first output based on a first probability that an image was inserted in a video. The first detector is trained with a set of known images to detect the set of known images. A second detector generates a second output based on a second probability that an image was inserted in the video. The second detector is used to detect the set of unknown images without training. The method analyzes the first output from the first detector based on the probability of the image existing in the video and the second output from the second detector based on the probability of the image existing in the video to generate a combined score from the first output and the second output. An indication of whether the image is detected in the video is output based on the combined score.

Abstract: In some embodiments, a first detector generates a first output based on a first probability that an image was inserted in a video. The first detector is trained with a set of known images to detect the set of known images. A second detector generates a second output based on a second probability that an image was inserted in the video. The second detector is used to detect the set of unknown images without training. The method analyzes the first output from the first detector based on the probability of the image existing in the video and the second output from the second detector based on the probability of the image existing in the video to generate a combined score from the first output and the second output. An indication of whether the image is detected in the video is output based on the combined score.