Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Workshop 1C: Student Presentation: Decision-Making Swarms

While swarms that execute decisions are well known in the swarm community, swarms that exhibit this capability a priori have never before been achieved. We demonstrate a methodology, based on the Hamiltonian method of swarm design, that enables the design and implementation of swarms that exhibit decision-making capability. We develop the theoretical structure of the method and apply it to the development of an ant algorithm and a swarm capable of deciding whether its density exceeds a specific predetermined value. The swarm designs are validated in simulation

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Apoptosis of human tongue squamous cell carcinoma cell (CAL-27) induced by Lactobacillus sp. A-2 metabolites

Objective: To study the effect of Lactobacillus sp. A-2 metabolites on viability of CAL-27 cells and apoptosis in CAL-27 cells. Methods: Lactobacillus sp. A-2 metabolites 1 and 2 (LM1 and LM2) were obtained by culturing Lactobacillus sp. A-2 in reconstituted whey medium and whey-inulin medium; the cultured CAL-27 cells were treated with different concentrations of LM1 and LM2 (0, 3, 6, 12, 24, 48 mg/mL) and assayed by methyl thiazolyltetrazolium (MTT) method; morphological changes of apoptotic cell were observed under fluorescence microscopy by acridine orange (Ao) fluorescent staining; flow cytometry method (FCM) and agarose gel electrophoresis were used to detect the apoptosis of CAL-27 cells treated LM1 and LM2. Results: The different concentrations of LM1 and LM2 could restrain the growth of CAL-27 cells, and in a dose-dependent manner; the apoptosis of CAL-27 cells was obviously induced and was time-dependent. Conclusions: Viability of CAL-27 cells was inhibited by Lactobacillus sp. A-2 metabolites; Lactobacillus sp. A-2 metabolites could induce CAL-27 cells apoptosis; study on the bioactive compounds in the Lactobacillus sp. A-2 metabolites and their molecular mechanism is in progress

Dense metal corrosion depth estimation

Introduction: Metal corrosion detection is important for protecting lives and property. X-ray inspection systems are widely used because of their good penetrability and visual presentation capability. They can visually display both external and internal corrosion defects. However, existing X-ray-based defect detection methods cannot present and estimate the dense corrosion depths. To solve this problem, we propose a dense metal corrosion depth estimation method based on image segmentation and inpainting.Methods: The proposed method employs an image segmentation module to segment metal corrosion defects and an image inpainting module to remove these segmented defects. It then calculates the pixel-level dense corrosion depths using the X-ray images before and after inpainting. Moreover, to address the difficulty of acquiring training images with ground-truth dense corrosion depth annotations, we propose a virtual data generation method for creating virtual corroded metal X-ray images and their corresponding ground-truth annotations.Results: Experiments on both virtual and real datasets show that the proposed method successfully achieves accurate dense metal corrosion depth estimation.Discussion: In conclusion, the proposed virtual data generation method can provide effective and sufficient training samples, and the proposed dense metal corrosion depth estimation framework can produce accurate dense corrosion depths