ABSTRACT With the wide spread of Internet and e‐commerce, the online shopping market has attracted more and more customers. Under this background, the service quality of online shopping platforms has become more and more important. A high level of service quality leads to higher customer loyalty and increases sale profits. In this paper, we focus on the service quality management of online shopping platforms. Firstly, we analyze the service problems of online shopping platforms by topic mining, according to the customer complaint text. Secondly, we analyze the general purchase processes of online shopping platforms by using process chain network (PCN), a tool for service analysis. Finally, we optimize the purchase processes of online shopping platforms based on the discovered service problems and PCN. Our paper contributes to the area of service management by presenting a data‐driven methodology for service quality management of online shopping platforms. Furthermore, our methodology can be generalized for the service quality management of various domains.

Scalable deep Super-Resolution (SR) models are increasingly in demand, whose memory can be customized and tuned to the computational recourse of the platform. The existing dynamic scalable SR methods are not memory-friendly enough because multi-scale models have to be saved with a fixed size for each model. Inspired by the success of Lottery Tickets Hypothesis (LTH) on image classification, we explore the existence of unstructured scalable SR deep models, that is, we find gradual shrinkage subnetworks of extreme sparsity named winning tickets. In this paper, we propose a Memory-friendly Scalable SR framework (MSSR). The advantage is that only a single scalable model covers multiple SR models with different sizes, instead of reloading SR models of different sizes. Concretely, MSSR consists of the forward and backward stages, the former for model compression and the latter for model expansion. In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets. Moreover, stochastic self-distillation (SSD) is conducted to boost the performance of sub-networks. By stochastically selecting multiple depths, the current model inputs the selected features into the corresponding parts in the larger model and improves the performance of the current model based on the feedback results of the larger model. In the backward stage, the smaller SR model could be expanded by recovering and fine-tuning the pruned parameters according to the pruning-out masks obtained in the forward. Extensive experiments show the effectiveness of MMSR. The smallest-scale sub-network could achieve the sparsity of 94% and outperforms the compared lightweight SR methods.

Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing the uncertainty of them. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first introduce an Uncertainty Estimation Block (UEB) to predict the aleatoric and epistemic uncertainty together. Then, we propose an Uncertainty-aware Feature Modulation (UFM) block to adaptively enhance the learned features. UFM predicts a convolution kernel and channel-wise modulation cofficients conitioned on the uncertainty weighted representation. Moreover, we develop an uncertainty-driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one. Extensive experimental results on synthetic datasets and real-world images show that UDN achieves significant quantitative and qualitative improvements, outperforming the state-of-the-arts.

Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can recognize objects from both seen and unseen classes. Due to the absence of unseen training samples, the classifier tends to bias towards seen classes. To mitigate this problem, feature generation based models are proposed to synthesize visual features for unseen classes. However, these features are generated in the visual feature space which lacks of discriminative ability. Therefore, some methods turn to find a better embedding space for the classifier training. They emphasize the inter-class relationships of seen classes, leading the embedding space overfitted to seen classes and unfriendly to unseen classes. Instead, in this paper, we propose an Intra-Class Compactness Enhancement method (ICCE) for GZSL. Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space. By promoting the intra-class relationships but the inter-class structures, we can distinguish different classes with better generalization. Specifically, we propose a Self-Distillation Embedding (SDE) module and a Semantic-Visual Contrastive Generation (SVCG) module. The former promotes intra-class compactness in the embedding space, while the latter accomplishes it in the visual feature space. The experiments demonstrate that our ICCE outperforms the state-of-the-art methods on four datasets and achieves competitive results on the remaining dataset.

Cross-modality person re-identification is a newly rising and challenging problem, as there is a significant gap between the visible and infrared images. Though recent methods rapidly narrow the gap, the intra-modality variance is often ignored before inter-modality alignment. In this paper, we study this problem in the knowledge distillation perspective and design a self-mimic mutual-distillation method to reduce the discrepancy of each person from intra-modality feature alignment to cross-modality feature alignment. For intra-modality feature alignment, the self-mimic mechanism is implemented to simultaneously learn globally viewed, stable, and distinguish prototypes for each ID and minimize the intra-modality discrepancy. For inter-modality feature alignment, the mutual distillation is conducted to minimize the cross-modality distribution discrepancy of each person. Extensive experimental results on SYSU-MM01 and RegDB demonstrate that the proposed method achieves the best performance, outperforming state-of-the-art methods by a large margin without adding extra network parameters to the baseline. Especially, on the SYSU-MM01 dataset, our method achieves 64.8% Rank-1 and 60.2% mAP with significant gains over the latest related method.

State-of-the-art deep dehazing models are often difficult in training. Knowledge distillation paves a way to train a student network assisted by a teacher network. However, most knowledge distill methods are used for image classification and segmentation as well as object detection, and few investigate distilling image restoration and use different task for knowledge transfer. In this paper, we propose a knowledge-distill dehazing network which distills image dehazing with the heterogeneous task imitation. In our network, the teacher is an off-the-shelf auto-encoder network and is used for image reconstruction. The dehazing network is trained assisted by the teacher network with the process-oriented learning mechanism. The student network imitates the task of image reconstruction in the teacher network. Moreover, we design a spatial-weighted channel-attention residual block for the student image dehazing network to adaptively learn the content-aware channel level attention and pay more attention to the features for dense hazy regions reconstruction. To evaluate the effectiveness of the proposed method, we compare our method with several state-of-the-art methods on two synthetic and real-world datasets, as well as real hazy images.

This study investigates the impacts of knowledge building on students’ views on collaboration. Participants were 53 fifth graders. Data mainly came from students’ online activities and pre-post interview with regard to students’ view on collaboration. Findings indicate that engaging students in knowledge building helped broaden students’ view of collaboration, enabling them to see collaboration not just from a task-driven, group-based perspective, but also from a more idea-centered perspective.