| 1. |
- Liang, Xiubo, et al.
(författare)
-
Menstrual Monster : A Tangible Interactive Co-educational Game Designed for Teenagers
- 2022
-
Ingår i: CHI EA '22. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450391566
-
Konferensbidrag (refereegranskat)abstract
- Learning menstruation in early adolescence could reduce teenagers’ misunderstanding of it and help them treat menstruation in a proper way. This paper explored a tangible game for teenagers of different genders learning menstruation through collaborative playing. The game included five levels where users play together and learn the cause, products, symptoms of menstruation as well as try to judge some scenarios and listen to audios about menstruation. In our user study, we invited three groups of teenagers ages 11 to 16. Each group contained at least one male and one female, and we let them play the game freely. Teenagers were successfully able to play the game collaboratively, learn menstruation-related knowledge. The results revealed motivation differences related to gender, and after the game, teenagers demonstrated the observable change of the attitude towards menstruation.
|
|
| 2. |
- Ma, Shuai, et al.
(författare)
-
Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction
- 2020
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 7, s. 185099-185107
-
Tidskriftsartikel (refereegranskat)abstract
- Track quality evaluation is fundamental for track maintenance. Around the world, track geometry standards are established to evaluate track quality. However, these standards may not be capable of detecting some abnormal track geometry conditions that can cause considerable vehicle-body vibration. And people gradually realized that track quality evaluation should be based not only on track geometry but also on vehicle performance. Vehicle-body vibration prediction is beneficial for locating potential track geometry defects, and the predicted accelerations can be used as an auxiliary index for assessing track quality. For this purpose, this paper gives a method to predict vehicle-body vibration based on deep learning, which represents one of the newest areas in artificial intelligence. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is proposed to make accurate and point-wise prediction. To achieve the optimal performance and explore the internal mechanism of the model, structural configurations and inner states are extensively studied. CNN-LSTM can take advantage of the powerful feature extraction capacity of CNN and LSTM, and outperforms the fully-connected neural network and the plain LSTM on the experimental data of a high-speed railway. In detail, CNN-LSTM has superior performance in predicting vertical vehicle-body vibration below 10 Hz and lateral vehicle-body vibration below 1 Hz. Moreover, analysis shows that the predicted vehicle-body acceleration can act as a performance-based evaluation index of track quality.
|
|
| 3. |
- Nie, Jintao, et al.
(författare)
-
SignFind: A Synchronized Sign Language and Chinese Character Teaching Game for Chinese Deaf Children Using Gesture Recognition
- 2022
-
Ingår i: CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450391566 ; , s. 1-7
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a targeted system to help Chinese deaf children learn both sign language and Chinese characters in early language learning. The system combines sign recognition and in-air writing techniques with games so that children can practice sign language and Chinese character writing at the same time. Sign recognition is used to assess the accuracy of sign language and in-air writing records the process of writing Chinese characters. In addition, the game adds to the learning fun and makes children more willing to complete learning tasks. We have developed a prototype to evaluate the effectiveness of a simultaneous Chinese sign language and Chinese character teaching system based on gesture recognition and in-air writing. We expect that this system will increase children's willingness and efficiency to learn sign language and Chinese characters, and eventually be used to assist in early language education for deaf children in the Chinese region.
|
|
