We explored the critical factors associated with iLearning that impact students’ learning performance and identified the factors with a notable influence to help managers in higher education institutions increase the effectiveness of iLearning for students. We initially synthesised 4 main dimensions (including 26 criteria): performance expectancy, lecturers’ influence, quality of service, and personal innovativeness. Subsequently, we conducted surveys in two stages. First, by studying a group of students with experience using iLearning at Taiwanese universities, we extracted 5 critical dimensions (including 18 criteria) through a factor analysis. Second, by studying a group of senior educators and practitioners in Taiwan, we prioritised the dimensions and criteria through the analytic hierarchy process (AHP). We found that performance expectancy is the top critical dimension, and the top five critical criteria pertain to enhancing the learning performance, increasing the learning participation, altering learning habits, ensuring access at all times, and enabling prompt use of learning resources. Moreover, we recommend several suggestions for the relevant managers to enhance the students’ iLearning performance.

We propose the Dual-Generator (DG) network for largepose face reenactment. Given a source face and a reference face as inputs, the DG network can generate an output face that has the same pose and expression as the reference face, and has the same identity as the source face. As most approaches do not particularly consider large-pose reenactment, the proposed approach addresses this issue by incorporating a 3D landmark detector into the framework and considering a loss function to capture visible local shape variation across large pose. The DG network consists of two modules, the ID-preserving Shape Generator (IDSG) and the Reenacted Face Generator (RFG). The IDSG encodes the 3D landmarks of the reference face into a reference landmark code, and encodes the source face into a source face code. The reference landmark code and the source face code are concatenated and decoded to a set of target landmarks that exhibits the pose and expression of the reference face and preserves the identity of the source face. The RFG is partially built on the StarGAN2 generator with modifications on the input and layer settings, and with a facial style encoder added in. Given the target landmarks made by the IDSG and the source face as inputs, the RFG generates the target face with the desired identity, pose and expression. We evaluate our approach on the RaFD, MPIE, VoxCeleb1, and VoxCeleb2 benchmarks and compare with state-of-the-art methods.

Visual grounding aims to localize a target object in an image based on a given text description. Due to the innate complexity of language, it is still a challenging problem to perform reasoning of complex expressions and to infer the underlying relationship between the expression and the object in an image. To address these issues, we propose a residual graph attention network for visual grounding. The proposed approach first builds an expression-guided relation graph and then performs multi-step reasoning followed by matching the target object. It allows performing better visual grounding with complex expressions by using deeper layers than other graph network approaches. Moreover, to increase the diversity of training data, we perform an expression-respect data augmentation based on copy-paste operations to pairs of source and target images. The proposed approach achieves better performance with extensive experiments than other state-of-the-art graph network-based approaches and demonstrates its effectiveness.

Thanks to GPU-accelerated processing, cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to the emerging pandemic. Compared to the Bayesian-based 2D classification widely used in the work-flow, the multi-reference alignment (MRA) is less popular. It is time-consuming despite its superior in differentiating structural variations. Interestingly, the Bayesian approach has higher complexity than MRA. We thereby reason that the popularity of Bayesian is gained through GPU acceleration, where a modular acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.

Unlike most face reenactment that deals with minor pose variation, we propose the Pose-Guided Reenactment (PGR) framework for large-pose face reenactment. The proposed PGR is composed of a landmark detector, a landmark encoder, a face encoder, a landmark decoder, a style encoder, a face generator and two discriminators. The training of these components is divided into two phases. In Phase I, the landmark encoder is trained to encode the landmarks of the reference face to a reference landmark code, and the face encoder is trained to encode the source face to a source face code. The reference landmark code and the source face code are entered to the landmark decoder, which generates a target landmark set. In Phase II, the face generator is trained to generate the target face with the desired identity, pose and expression, given the target landmark set and the source face as input. To handle large pose, we include the large pose data in the training set and propose the pose-guided landmark switch to control the change of the facial landmarks during pose variation. Experiments on MPIE and VoxCeleb1 benchmark databases show that the proposed PGR can effectively reenact the faces with large pose, and delivers a state-of-the-art overall performance compared with other approaches.