Electronic shopping has become an important way of shopping in our daily life. A good recommendation system can greatly improve the consumer shopping experience through alleviating consumer and product information overload problem. In the real scenario, each consumer has a wide variety of interests, and consumers interests are constantly changing. Many recent models usually give an overall embedding for consumer behavior sequence or only capture features of customers and characteristics of products from single perspective. However, these methods can hardly model consumers multiple interests and evolving of interests completely. Thus, in this study, we consider both the diversity and dynamics of consumer interests, and model them in a unified model. In light of this, we propose a novel model named Deep Self-Attention Mulit-Interest Networks (DSMN), which captures latent multiple interests by using capsule networks. It can perceive evolving of interests from consumer behavior sequences by using attention mechanism. We evaluate our model on three real-world datasets. In the experiments, our DSMN is 4.934% higher than the average of competitors in terms of AUC. Compared to our original silver medal method on the Kaggle competition, our DSMN is 18.978% higher in terms of MAP@12.

The quantity security of stored grain in warehouses is crucial for a country. Since manual inspection of stored grain quantity is inefficient, some online automeasurement methods have been proposed recently, but they are either time-consuming or too expensive to be popularized. This article proposes a deep learning-based method to automatically monitor changes in grain quantity of granaries. First, the image of the same scene in a granary is taken at different moments using a device that integrates a camera and an infrared laser rangefinder. Then, a deep semantic segmentation model based on an encoder-decoder framework is developed to extract the grain loading line and grain surface of the image. Finally, the distance and area between the extracted grain loading line and grain surface are calculated and compared with the previous measured one to determine whether the grain quantity of the granary has changed. To improve the accuracy of the segmentation results, a novel reverse attention model is proposed to provide guidance information to fuse low-level features, which calculates multiscale attention maps based only on the output of the last layer of the encoder. Furthermore, the proposed method and infrared laser rangefinder-based method are combined to get more accurate grain volume after an abnormal change of grain quantity is detected by our method. Experimental results show that our method is effective and feasible for monitoring changes in grain quantity and outperforms the state-of-the-art methods on semantic segmentation.

An important research aspect of Spectrum-Based Fault Localization (SBFL) is the influence factors of the effectiveness of suspiciousness formulas from the perspective of symmetry. Coincidental correctness is one of the most important factors impacting the effectiveness of suspiciousness formulas. The influence of fault localization by coincidental correctness has attracted a large amount of research in the perspective of empirical study; however, it can hardly be considered as sufficiently comprehensive when there are a large number of the symmetrical suspiciousness formulas. Therefore, we first develop an innovative theoretical framework with function derivation investigating suspiciousness formulas impacted by coincidental correctness. We define three types of relations between formulas affected by coincidental correctness: namely, improved type, invariant type and uncertain type. We investigated 30 suspiciousness formulas using this framework and group them into three categories. Furthermore, we conduct an empirical study to verify the effectiveness of SBFL affected by coincidental correctness on four relatively large C programs. We proved that coincidental correctness has a positive effect on 23 out of these 30 formulas, no effect on 5 of them, and the effect on the remaining 2 of them depend on certain conditions. The experimental results show that the effectiveness of some suspiciousness formulas can be enhanced and that of some suspiciousness formulas remain unchanged.