Knowledge tracing has been widely used in online learning systems to guide the students’ future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model’s ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: First, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Second, we design an Emotional State Tracing Module to monitor students’ personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students’ response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learning-based Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance.

Sibling-head structure is widely used to alleviate the feature conflict between classification and regression tasks in most object detectors. However, as the two branches of the sibling head are trained with exactly the same positive samples and lack explicit feature disentanglement in the forward propagation, the classification-sensitive features and localization-sensitive features are still somewhat coupled. As a result, the feature conflict between the two tasks still remains, which seriously hurts the performance of the classifier and regressor in the testing phase. In this paper, we propose a Task-Aware Disentangled object Detector (TDD) that explicitly disentangles the classification and regression from the aspect of feature disentanglement and sampling strategy. In terms of feature disentanglement, we design a task-aware activation head driven by a reconstruction-activation mechanism to explicitly activate corresponding sensitive features for classification and localization in the forward propagation. Furthermore, we explore a novel task-aware sampling strategy that explicitly assigns the task-adaptive samples for classification and regression tasks according to their quality distributions. Extensive experiments on MS COCO show that our TDD consistently surpasses the baseline by ~2.0 AP with different backbones. Moreover, our best model achieves 55.1 AP, outperforming most state-of-the-art detectors.

Classification and regression are two pillars of object detectors. In most CNN-based detectors, these two pillars are optimized independently. Without direct interactions be-tween them, the classification loss and the regression loss can not be optimized synchronously toward the optimal direction in the training phase. This clearly leads to lots of inconsistent predictions with high classification score but low localization accuracy or low classification score but high localization accuracy in the inference phase, especially for the objects of irregular shape and occlusion, which severely hurts the detection performance of existing detectors after N-MS. To reconcile prediction consistency for balanced object detection, we propose a Harmonic loss to harmonize the optimization of classification branch and localization branch. The Harmonic loss enables these two branches to supervise and promote each other during training, thereby producing consistent predictions with high co-occurrence of top classification and localization in the inference phase. Furthermore, in order to prevent the localization loss from being dominated by outliers during training phase, a Harmonic IoU loss is proposed to harmonize the weight of the localization loss of different IoU-level samples. Comprehensive experiments on benchmarks PASCAL VOC and MS COCO demonstrate the generality and effectiveness of our model for facilitating existing object detectors to state-of-the-art accuracy.

The anchor-based detectors handle the problem of scale variation by building the feature pyramid and directly setting different scales of anchors on each cell in different layers. However, it is difficult for box-wise anchors to guide the adaptive learning of scale-specific features in each layer because there is no one-to-one correspondence between box-wise anchors and pixel-level features. In order to alleviate the problem, in this paper, we propose a scale-customized weak segmentation (SCWS) block at the pixel level for scale customized object feature learning in each layer. By integrating the SCWS blocks into the single-shot detector, a scale-aware object detector (SCOD) is constructed to detect objects of different sizes naturally and accurately. Furthermore, the standard location loss neglects the fact that the hard and easy samples may be seriously imbalanced. A forthcoming problem is that it is unable to get more accurate bounding boxes due to the imbalance. To address this problem, an adaptive IoU (AIoU) loss via a simple yet effective squeeze operation is specified in our SCOD. Extensive experiments on PASCAL VOC and MS COCO demonstrate the superiority of our SCOD.

In the CNN based object detectors, feature pyramids are widely exploited to alleviate the problem of scale variation across object instances. These object detectors, which strengthen features via a top-down pathway and lateral connections, are mainly to enrich the semantic information of low-level features, but ignore the enhancement of high-level features. This can lead to an imbalance between different levels of features, in particular a serious lack of detailed information in the high-level features, which makes it difficult to get accurate bounding boxes. In this paper, we introduce a novel two-pronged transductive idea to explore the relationship among different layers in both backward and forward directions, which can enrich the semantic information of low-level features and detailed information of high-level features at the same time. Under the guidance of the two-pronged idea, we propose a Two-Pronged Network (TPNet) to achieve bidirectional transfer between high-level features and low-level features, which is useful for accurately detecting object at different scales. Furthermore, due to the distribution imbalance between the hard and easy samples in single-stage detectors, the gradient of localization loss is always dominated by the hard examples that have poor localization accuracy. This will enable the model to be biased toward the hard samples. So in our TPNet, an adaptive IoU based localization loss, named Rectified IoU (RIoU) loss, is proposed to rectify the gradients of each kind of samples. The Rectified IoU loss increases the gradients of examples with high IoU while suppressing the gradients of examples with low IoU, which can improve the overall localization accuracy of model. Extensive experiments demonstrate the superiority of our TPNet and RIoU loss.

Driven by the pressing needs in coordinating and synchronizing multi-plant facilities for efficient production and manufacturing, distributed assembly permutation flowshop scheduling problem (DAPFSP) has been becoming the focus of concern of evolutionary computing and operations research, which is a typical NP-hard combinatorial optimization problem. In this paper, we propose a novel generalized version of DAPFSP, where multiple assembly factories exist rather than only one assembly factory in the conventional DAPFSP, meanwhile no-wait constraint exists in the processing stage. We name this new model as the distributed multiple assembly permutation flowshop scheduling problem with no-wait, abbreviated as DMAPFSP-NW. We propose hybrid iterated local search with simulated annealing (ILS-SA) for the proposed scheduling model. Simulation results based on 27 large-scale benchmark problems show that our proposed ILS-SA can effectively solve the DMAPFSP-NW.