Self-Attention Based Time-Rating-Aware Context Recommender System.

The sequential recommendation can predict the user's next behavior according to the user's historical interaction sequence. To better capture users' preferences, some sequential recommendation models propose time-aware attention networks to capture users' long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user's displayed feedback (rating) on an item reflects the user's preference for the item, which directly affects the user's choice of the next item to a certain extent. In different periods of sequential recommendation, the user's rating of the item reflects the change in the user's preference. In this paper, we separately model the time interval of items in the user's interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users' preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.

Corrigendum to "Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm".

[This corrects the article DOI: 10.1155/2021/6653659.].

Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm.

Emotion recognition is a research hotspot in the field of artificial intelligence. If the human-computer interaction system can sense human emotion and express emotion, it will make the interaction between the robot and human more natural. In this paper, a multimodal emotion recognition model based on many-objective optimization algorithm is proposed for the first time. The model integrates voice information and facial information and can simultaneously optimize the accuracy and uniformity of recognition. This paper compares the emotion recognition algorithm based on many-objective algorithm optimization with the single-modal emotion recognition model proposed in this paper and the ISMS_ALA model proposed by recent related research. The experimental results show that compared with the single-mode emotion recognition, the proposed model has a great improvement in each evaluation index. At the same time, the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The experimental results show that the many-objective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

Preparation of Modified Chitosan Microsphere-Supported Copper Catalysts for the Borylation of α,ß-Unsaturated Compounds.

Chitosan microspheres modified by 2-pyridinecarboxaldehyde were prepared and used in the construction of a heterogeneous catalyst loaded with nano-Cu prepared by a reduction reaction. The chemical structure of the catalyst was investigated by Fourier Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and X-ray Photoelectron Spectroscopy (XPS). Under mild conditions, such as no ligand at room temperature, the catalyst was successfully applied to catalyze the borylation of α,ß-unsaturated receptors in a water-methanol medium, yielding 17%-100% of the corresponding -hydroxy product. Even after repeated use five times, the catalyst still exhibited excellent catalytic activity.

An in-line spectrophotometer on a centrifugal microfluidic platform for real-time protein determination and calibration.

In this paper, an in-line, low-cost, miniature and portable spectrophotometric detection system is presented and used for fast protein determination and calibration in centrifugal microfluidics. Our portable detection system is configured with paired emitter and detector diodes (PEDD), where the light beam between both LEDs is collimated with enhanced system tolerance. It is the first time that a physical model of PEDD is clearly presented, which could be modelled as a photosensitive RC oscillator. A portable centrifugal microfluidic system that contains a wireless port in real-time communication with a smartphone has been built to show that PEDD is an effective strategy for conducting rapid protein bioassays with detection performance comparable to that of a UV-vis spectrophotometer. The choice of centrifugal microfluidics offers the unique benefits of highly parallel fluidic actuation at high accuracy while there is no need for a pump, as inertial forces are present within the entire spinning disc and accurately controlled by varying the spinning speed. As a demonstration experiment, we have conducted the Bradford assay for bovine serum albumin (BSA) concentration calibration from 0 to 2 mg mL(-1). Moreover, a novel centrifugal disc with a spiral microchannel is proposed for automatic distribution and metering of the sample to all the parallel reactions at one time. The reported lab-on-a-disc scheme with PEDD detection may offer a solution for high-throughput assays, such as protein density calibration, drug screening and drug solubility measurement that require the handling of a large number of reactions in parallel.