The task of knowledge tracing aims to monitor students’ knowledge states through their historical answer records and predict their future performance in answering questions. In recent years, knowledge tracing models based on deep learning have exhibited significantly higher accuracy in prediction compared to traditional knowledge tracing models. However, existing models often fail to fully leverage the impact of difficulty factors on students’ knowledge states. In this paper, to better exploit the difficulty factors for enhancing the discriminative power among different questions, we propose a novel Difficulty-Fusion Knowledge Tracing(DFAKT) model. Specifically, we design a DEI(Difficult-Enhance Interaction) method for extracting difficulty features by integrating skill difficulty and question-specific difficulty information. Subsequently, we develop a Difficulty-Fusion module designed to integrate the student interaction information, which includes features such as answers and knowledge points, with the difficulty feature. Finally, recognizing that different exercises have varying impacts on students’ knowledge states, we incorporate an attention-based GRU module in the model to dynamically aggregate the knowledge states from previous time steps. This allows the model to focus more on questions with a greater impact on knowledge states, thereby improving prediction accuracy. Experimental results demonstrate that the proposed model outperforms existing models in predictive performance on large-scale real-world datasets, and the effectiveness of each module is validated.

Multimodal sentiment analysis is a hot topic in the field of multimodal research, which uses the multiple modal forms contained in a video (including speech, video, audio, etc.) to further obtain information such as textual information, facial expressions, and vocal intonation for sentiment understanding. The output obtained is analyzed for multimodal emotion, mainly through the complex fusion of different modal information. However, the inconsistent contribution of different modal information in multimodal networks and the undifferentiated fusion between modalities causes the fused multimodal information to contain a lot of noisy information. How to reduce the noise information between different modalities so that the multimodal contains rich and accurate messages is a challenging research task. In this paper, we propose gating mechanisms applied to within-modal and multitasking processes respectively to reduce the noise information. The intra-modal gating mechanism is a fusion control by discriminating the similarity between modalities after projecting features into the subspace. The multi-task gating mechanism is a loss calculation by discriminating the degree of difference between unimodal and multimodal labels. The performance of the downstream MSA task is improved by reducing noise information in many ways. Our gating mechanism can also be used as a framework to help improve the performance of other baseline models. Our experiments on the commonly used multimodal datasets CMU-MOSI, and CMU-MOSEI show that we achieve better performance than other good baseline models. We also consider the arrangement of the gating mechanism on the baseline model and demonstrate its performance improvement. In addition, we verify the effectiveness of the different modules through ablation experiments.

Multimodal sentiment analysis (MSA) aims to obtain the emotional polarity of language by analyzing multiple forms of human language, facial expressions, and vocal intonation. The traditional MSA model focuses on the fusion between modalities, ignoring the different contributions of language, visual, and acoustic. Thus different information of modality possesses different importance. To further explore the contributions of different modalities, we propose a highly generalized identifying modal contributions network(IMCN), which contains modality interaction module, modality fusion, and modality joint learning units in the framework. Specifically, we first designed a language modality gain detection module to make reasonable use of visual and acoustic information and reduce the noise of modal information. Secondly, crossmodal attention is used to enrich modal information. Finally, we perform joint learning of unimodal and multimodal modalities to explore the optimal solution for multimodal output. We compared with other popular multimodal sentiment analysis models and obtained better sentiment classification results on CMU-MOSI and CMU-MOSEI benchmark datasets. We also further validated the effectiveness of different modules of IMCN through ablation experiments and discussed the ideas of IMCN design.

Knowledge Tracing (KT) is an important part of intelligent online education and is the key to personalized guidance for students' learning process. It aims at dynamically estimate students' knowledge status based on history answer records and predict whether they will answer the next question correctly. Predicting students' knowledge is a difficult task because student learning is a dynamic process and students' knowledge status is constantly changing. However, existing approaches often ignore the fact of students' stage development of learning ability and do not take into account the impact of short-term knowledge states on the next moment. Existing models do not emphasize the importance of students' long-term knowledge states and short-term knowledge states. In this paper, we propose a new Long- and Short-term Attention network Knowledge Tracing model (LSAKT). Specifically, we divided the sequences into subsequences based on time stamps, with the first attention layer learning the student's long-term knowledge state based on the interactional performance with the problem, while the other attention layer learns the student's short-term knowledge state based on the last sequence. Finally, we integrate the long-term knowledge state and the short-term knowledge state to form the student's final knowledge state. We evaluated the proposed model on four publicly available datasets, and the experimental results showed that the LSAKT approach achieved a fantastic result.

An interconnection network, RP(k), and its properties are investigated. Two parameters, the closest group and the optimal partition of networks are proposed. It is shown that RP(k) network has high communication efficiency . It is also proven that RP(k) network is a Hamiltonian graph and the ring can be embedded into the RP(k) network with load, expansion, dilation and congestion all equal to 1 even though some faulty nodes exist in the RP(k) network. Embedding of Rings and 2-D meshes into the RP(k) networks are discussed. Two embedding methods are given with high embedding performance.