For a long time, expressions have been something that human beings are proud of. That is an essential difference between us and machines. With the development of computers, we are more eager to develop communication between humans and machines, especially communication with emotions. The emotional growth of computers is similar to the growth process of each of us, starting with a natural, intimate, and vivid interaction by observing and discerning emotions. Since the basic emotions, angry, disgusted, fearful, happy, neutral, sad and surprised are put forward, there are many researches based on basic emotions at present, but few on compound emotions. However, in real life, people’s emotions are complex. Single expressions cannot fully and accurately show people’s inner emotional changes, thus, exploration of compound expression recognition is very essential to daily life. In this paper, we recommend a scheme of combining spatial and frequency domain transform to implement end-to-end joint training based on model ensembling between models for appearance and geometric representations learning for the recognition of compound expressions in the wild. We are mainly devoted to digging the appearance and geometric information based on deep learning models. For appearance feature acquisition, we adopt the idea of transfer learning, introducing the ResNet50 model pretrained on VGGFace2 for face recognition to implement the fine-tuning process. Here, we try and compare two minds, one is that we utilize two static expression databases FER2013 and RAF Basic for basic emotion recognition to fine tune, the other is that we fine tune the model on the input three channels composed of images generated by DWT2 and WAVEDEC2 wavelet transforms based on rbio3.1 and sym1 wavelet bases respectively. For geometric feature acquisition, we firstly introduce a densesift operator to extract facial key points and their histogram descriptions. After that, we introduce deep SAE with a softmax function, stacked LSTM and Sequence-to-Sequence with stacked LSTM and define their structures by ourselves. Then, we feed the salient key points and their descriptions into three models to train respectively and compare their performances. When the model training for appearance and geometric features learning is completed, we combine the two models with category labels to achieve further end-to-end joint training, considering that ensembling models, which describe different information, can further improve recognition results. Finally, we validate the performance of our proposed framework on an RAF Compound database and achieve a recognition rate of 66.97%. Experiments show that integrating different models, which express different information, and achieving end-to-end training can quickly and effectively improve the performance of the recognition.

The thesis first gives a brief introduction about the concepts of blog, RSS and knowledge management. And it states the possibility and superiority of blog and RSS to explain the reason why we apply them together in the field of postgraduatepsilas knowledge management. Then, combined with the cultivating model in China and postgraduatespsila own learning characteristics, both the problems and cruxes existing in postgraduatespsila personal knowledge management are pointed out. Furthermore, to counter these problems, it proposed some instructive strategies with concrete implement process about how to make use of blog and RSS to improve postgraduate's personal knowledge management by setting up a knowledge management platform based on blog and RSS. These strategies have covered different aspects of knowledge management including knowledge identification, knowledge acquisition, knowledge development, knowledge sharing, knowledge application and knowledge evaluation. Finally, an available and effective way of postgraduate's personal knowledge management would be presented.

During physical therapy intervention protocols, it’s important to consider the individual’s intrinsic motivation to perform in-home recommended exercises. Physical therapy exercises can become tedious thus limiting the individual’s progress. Not only have researchers developed serious gaming systems to increase user motivation, but they have also worked on the design aesthetics since results have shown positive effects on the users’ performance for attractive models. As such, we improved the aesthetics of a previously developed serious game called Super Pop \(VR^{TM}\). Namely, we improved the game graphics, added new game features, and allowed for more game options to provide users the opportunity to tailor their own experience. The conducted user studies show that participants rank the version of the game with the improved aesthetics higher in terms of the amount of interest/enjoyment it generates, thus allowing for an increase in intrinsic motivation when interacting with the system.