Programming Knowledge Tracing (PKT) aims to estimate students' programming proficiency by analyzing their historical coding activities. It is important to leverage interactive information for PKT code representations, as code submissions are problem-specific interactive solutions evaluated by Online Judge (OJ) feedback. However, most existing PKT models only rely on pre-trained language models to capture static code information, overlooking feedback scores and problem-specific context. To address this issue, we propose an Interactive Information integrated Code Embedding for Programming Knowledge Tracing (IICE-PKT). This model learns a unified code representation by integrating comprehensive interactive information, including code text, OJ feedback scores, problem statement text, problem-skill correlations, and problem difficulties. IICE-PKT consists of three modules: Problem Representation module generates enhanced problem embeddings based on problem-skill correlations and problem difficulties; Code Representation module combines fine-tuned CodeBERT-based code text embeddings, GPT-generated problem text embeddings, and enhanced problem embeddings with supervised feedback scores to produce a unified code representation; Dual-Sequence Modeling module employs two independent GRUs to separately model the problem sequence and the code sequence. Extensive experiments demonstrate the superiority of IICE-PKT. Ablation studies confirm that integrating interactive information into code representations significantly enhances effectiveness.

It is necessary for the music-to-dance generation to consider both the kinematics in dance that is highly complex and non-linear and the connection between music and dance movement that is far from deterministic. Existing approaches attempt to address the limited creativity problem, but it is still a very challenging task. First, it is a long-term sequence-to-sequence task. Second, it is noisy in the extracted motion keypoints. Last, there exist local and global dependencies in the music sequence and the dance motion sequence. To address these issues, we propose a novel autoregressive generative framework that predicts future motions based on past motions and music. This framework contains a music conformer, a motion conformer, and a cross-modal conformer, which utilizes the conformer to encode music and motion sequences, and further adapt the cross-modal conformer to the noisy dance motion data that enable it to not only capture local and global dependencies among the sequences but also reduce the effect of noisy data. Quantitative and qualitative experimental results on the publicly available music-to-dance dataset demonstrate our method improves greatly upon the baselines and can generate long-term coherent dance motions well-coordinated with the music.

Automatic image colorization without manual interventions is an ill-conditioned and inherently ambiguous problem. Most of existing methods focus on formulating colorization as a regression problem and learn parametric mappings from grayscale to color through deep neural networks. Due to the multimodalities of color-grayscale space, in many applications, it is not required to recover exact ground-truth color. Pair-wise pixel-to-pixel learning-based algorithms lack rationality. Techniques such as color space conversion techniques are then proposed to avoid such direct pixel learning. However, the coloring results after color space conversion are blunt and unnatural. In this paper, we hold viewpoints that a reasonable solution is to generate some colorized result that looks natural. No matter what color a region is to be assigned, the colorized region should be semantically and spatially consistent. In this paper, we propose an effective semantic-aware automatic colorization model via unpaired cycle-consistent self-supervised network. Low-level monochrome loss, perceptual identity loss and high-level semantic-consistence loss, together with adversarial loss, are introduced to guide network self-training. We train and test our model on randomly selected subsets from PASCAL VOC 2012. The experimental results including human subjective studies demonstrate that, compared with state-of-the-art methods, our proposed model can achieve more convincing and superior results. Relevant source code is available at https://github.com/YuSuen/ACCycleGAN.

As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi-coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real-world optimization problem, and four well-established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

Latent Semantic Indexing (LSI) has been successfully applied to information retrieval and text classification. However, when LSI is used in classification, some important features for small classes may be ignored because of their small feature values. To solve this problem, we propose the latent semantic classification (LSC) model which extends the LSI model in the following way: the classification information of the training documents is introduced into the latent semantic structure via a second set of latent variables, so that both indexing and classification information can be taken into account during the classification process. Our experiments on Reuters show that our new model performs better than the existing classification methods such as kNN and SVM.