Constructing the Phrase Dictionary and Visualizing Consumer Behaviors in the Food Industry Based on Online Reviews during the COVID-19 Pandemic

The COVID-19 pandemic affects food industrylargely. In this study, the data of online reviewsare collected from dianping.com during the outbreak and stable period of the COVID-19 pandemic in China, and the rules in combination with the statistical methods are adopted to train the dictionary ofrestaurant phrases.After the K-means algorithm is adopted to cluster the phrases in the dictionary, and the cluster class tags are defined, the co-occurrence analysis and the wordcloud analysis are conducted on the reviews. As indicated from the results, consumers pay attention to the three basic elements (i.e., services, environments and tastes), as well as to the social distance between people; Consumers who are more concerned about the pandemic situation raise higher requirements on environmental health issues than ordinary consumers, and place stress on the acquisition of security. As revealed from the mentioned results, restaurants should primarily take measures to maintain safe social distance between people and raise more rigorous environmental hygiene requirements on the environment. This method is served as a reference for the further online reviews analysis and provides implications for the management of the restaurants in COVID-19 pandemic period.

Clustering of cancer data based on Stiefel manifold for multiple views

Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

Google Classroom: A Learning Media In Increasing Students' Motivation

The objective of this study is to know the effect google classroom as learning platform in increasing students’ learning motivation in non-formal education subject of students’ Early Childhood Education programs during the first semester STAIN Mandailing Natal 2020/2021. This study is an experimental research with descriptive analysis. The population of this study was 128 students and there are 25 students as respondents in the cluster class. Pre-test and Post-test were used as techniques in collecting data. To analyze the data, the researcehrs used SPSS 16. The result of this study showed that the mean of pre-test was 47.692 and Post-test was 76.154 which can be concluded that there is an increase in the students’ learning motivation in experimental group using Google Classroom

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of these datasets has proved to be valuable for biological and medical research and helpful to reveal data structures from multiple collections. However, such data often have small sample size and high dimension. It is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, the optimization problem solved by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same.Conclusion: Particularly, our approach has better performs when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

Profil Keterampilan Argumentasi Siswa Kelas X dan XI MIPA di SMA Batik 1 Surakarta pada Materi Keanekaragaman Hayati

The research aims to know the argumentation skill profile and the difference of students' argumentation skill between 10th MS (Match and Science) and 11th MS in Batik 1 Senior High School of Surakarta on Biodiversity material. The type of research is descriptive quantitative using survey method. Population research is all students or 400 students of class 10th and 11th MS in SMA Batik 1 Surakarta. Sampling technique using stratified proportional random sampling. The instrument to measure the skills of the user is using essay handbooks. Analysis of argumentability was using Toulmin's Argumentation Pattern (TAP). The data were analyzed using quantitative descriptive. The results showed the quality of the students' argumentation in one of the Private High School Surakarta in sufficient category. Percentage of achievement of each indicator is 68% claim, 60% warrant, 53% data, 45% s backing and 0% rebuttal. The result of t-test got sig. (2-tailed) about 0.002. therefore we can conclude that argumentation skill of 10th and 11th MS in SMA Batik 1 Surakarta have significant differences of class X and XI argumentation skill shows a significant differences, although the argumentation skill quality of both cluster class is enough.

A New Kind of Based on the Graph K-Means Clustering Initial Center Selection Algorithm

K-means clustering algorithm is simple and fast, and has more intuitive geometric meaning, which has been widely applied in pattern recognition, image processing and computer vision. It has obtained satisfactory results. But it need to determine the initial cluster class center before executing the k-means algorithm, and the choice of the initial cluster class center has a direct impact on the final clustering results. A selection algorithm is proposed, which based on figure node most magnanimous to determine the initial cluster class center of K-means clustering algorithm. The method compares with the selection algorithm of other initial cluster class center, which has a simple algorithm idea and low time complexity, and it is significantly better than other clustering arithmetic.

REMOTE PREPARATION OF TWO-QUBIT ENTANGLED STATE VIA ONE-DIMENSIONAL FOUR-QUBIT CLUSTER AND CLUSTER-CLASS STATES

We propose two protocols for remotely preparing a two-qubit entangled state, where the quantum channels take the form of one-dimensional four-qubit cluster and cluster-class states, respectively. The total success probability and classical communication cost are also calculated.