Recent progress in logic programming (e.g., the development of the Answer Set Programming paradigm) has made it possible to teach it to general undergraduate and even high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed an online answer set programming environment with a self contained file system and a simple interface, allowing users to write logic programs and perform several tasks over the programs.
However, the animate predicate is different from theirs (?? Can someone make the difference very specific in the design section?) and we feel that our design is more natural in modeling animation tasks ( future work is to study these two designs -- goes to future work part). There are a few other major differences between our work and theirs. We use SPARC (e.g., sorts etc make it easier more effective to write correct programs compared with Clingo @cite_5 or DLV cite...). Next, ASPviz requires two programs one for drawing animation while the other is for information needed by the drawing animation. We don't require them to be separate program although we encourage the methodology of organize one program into two parts. Finally, we provide an online environment instead of a standalone application with the advantage: very expensive to install and manage a standalone program in a teaching environment for genera students particularly younger students (e.g., high school ones) @cite_10 ). Our online version allows us to HTML canvas will significantly reduce the development efforts.
@cite_5 @cite_10
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We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our visual question answering (VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three advantages. First, executing programs on a symbolic space is more robust to long program traces. Our model can solve complex reasoning tasks better, achieving an accuracy of 99.8 on the CLEVR dataset. Second, the model is more data- and memory-efficient: it learns to perform well on a small number of training data; it can also encode an image into a compact representation and answer questions offline, using only 1 of the storage needed by the best competing methods. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step. Our model recovers the ground truth programs precisely. "If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5?" The answer is probably a "No." Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.