Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively.

HF-QSRAM: Half-Select Free Quaternary SRAM Design with Required Peripheral Circuits for IoT/IoVT Applications

By using CNFET technology in 3a 2 nm node using a proposed SQI gate, two split bit-lines QSRAM architectures have been suggested to address the issue of increasing demand for storage capacity in IoT/IoVT applications. Peripheral circuits such as a novel quaternary to binary decoder for QSRAM have been offered. Various simulations on temperature, supply voltage, and access frequency have been done to evaluate and ensure the performance of the proposed SQI gate, suggested cells, and quaternary to binary decoder. Moreover, 1000 Monte-Carlo analyses on the fabrication parameters have been done to classify read and write delay and standby power of proposed cells along with PDP of proposed quaternary to binary decoder. It is worth mentioning that the PDP of the proposed SQI gate, decoder, and average power consumption of suggested HF-QSRAM cell reached 0.92 aJ, 4.13 aJ, and 0.15 µW, respectively, which are approximately 80%, 91%, and 33% improvements in comparison with the best existing designs in the literature.

Design and Implementation of EFSC Algorithm for Hybrid Representation of Distributed Databases

In distributed database management systems, fragmenting base connections increases concurrency and hence system throughput for query processing. User queries use hybrid fragmentation methods focused on vector bindings, and deductive database implementations lack query-access-rule dependence. As a result, for hierarchical deductive information implementations, a hybrid fragmentation solution is used. The method considers the horizontal partition of base relations based on the bindings placed on user requests, then produces vertical fragments of the horizontally partitioned relations, and finally clusters rules based on attribute affinity and query and rule access frequency. The suggested fragmentation approach makes distributed deductive database structures easier to develop.

Bibliometric Review of the Most Actively Cited Russian Publications in the Scopus Database

The article presents a bibliometric review of the array of the of the most actively cited Russian publications in the Scopus database for the period 2010-2020. The criteria for studying the array of publications with the highest citation were the following: frequency distribution of publications by type; determination of the share in open access; frequency distribution of documents by quartiles of publications; frequency distribution by SciVal topics; frequency distribution by the degree of relevance of topics (percentiles); analysis of publications by the number of co-authors; Influence on field-weighted citation (FWCI); determination of the share of publications made with and without foreign participation; determination of the list of countries with which the greatest amount of work was carried out; distribution of publications by shares in various fields of knowledge; determination of the average number of co-authors per 1 publication; determination of leading organizations by the number of publications included in the top 550 by citation for the period 2010-2020.It was found that the vast majority of publications with the highest citations were issued by major international organizations, and most of them are concentrated in journals with a high open access rating, mainly in the first CiteScore quartile. The leading fields in terms of the number of most cited documents are Medicine, followed by physics and astronomy, as well as biochemistry, Genetics and molecular biology. It is shown that it is preferable to use SciVal themes, rather than the thematic area of journals. It is shown that when ranking publications by citation, one should focus on the influence of citation weighted by field, but not on the overall citation. The greatest citation of Russian publications is observed in works performed within the framework of MegaScience projects in international mega-co-authorship. The study of the array of the most actively cited Russian scientific publications is important both for monitoring the dynamics of their significance level and for developing a strategy to increase it.

Identifying customer priority for new products in target marketing: Using RFM model and TextRank

Target marketing is a key strategy used to increase the revenue. Among many methods that identify prospective customers, the recency, frequency, monetary value (RFM) model is considered the most accurate. However, no RFM study has focused on prospects for new product launches. This study addresses this gap by using website access data to identify prospects for new products, thereby extending RFM models to include website-specific weights. An RF model, built using frequency and recency information from website access data of customers, and an RwF model, built by adding website weights to frequency of access, were developed. A TextRank algorithm was used to analyze weights for each website based on the access frequency, thus defining the weights in the RwF model. South Korean mobile users’ website access data between May 1 and July 31, 2020 were used to validate the models. Through a significant lift curve, the results indicate that the models are highly effective in prioritizing customers for target marketing of new products. In particular, the RwF model, reflecting website-specific weights, showed a customer response rate of more than 30% among the top 10% customers. The findings extend the RFM literature beyond purchase history and enable practitioners to find target customers without a purchase history.

Correlations between Online Learning Media Types, First Access Time, Access Frequency, and Students’ Achievement in a Flipped Classroom Implementation

Since the 1960s, the world has seen how Information Technology (IT) influences education. In the present era, with the massive development of the Internet, various kinds of IT-assisted learning are popping up like mushrooms in the rainy season. However, no matter how advanced IT-assisted learning has been grown, learning media is still an inseparable part of education. In this study, we specifically present how the use of certain types of learning media correlated with students’ access behaviors and, more importantly, students’ achievement. The result shows that these factors have a positive correlation. In terms of media type influence towards students’ achievement, the media that has the appearance of the lecturer gives better achievement, compared to the media that only has audio, and the media that only consists of text and images.

The Effect of WeChat Usage on Upward Social Comparison in Undergraduates

Objective To explore the status of undergraduates’ WeChat usage and upward social comparison, and analyze the relationship between the above two variables.Method：Totally 754 WeChat undergraduates were selected through poster recruitment from 5 colleges in Guangdong Province. They were investigated with Access Frequency to Social Networking Site Scale (AFSNSS), Upward Social Comparison Subscale of Iowa-Nwtherlands Comparison Orientation Measure (INCOM-USCS). Results：The score of overall emotional engagement(OEE) of AFSNSS and the total standard score of INCOM–USCS (TSSI-U) were (22.41±4.70) and (0±4.5), respectively. The correlation between WeChat frequency and TSSI-U was not significant.TSSI-U was significantly postively correlated with the score of OEE and other items of AFSNSS (r=0.161～0.413, P <0.01). Multiple linear regression analysis showed that The scores of item 1,7and 8 of AFSNSS, as well as family economic status, grade ranking of academic performance (GRAP) and the purpose of WeChat use were postively correlated with TSSI-U (β=.104～.234, P<.05). Class cadre or not and origin were negatively correlated with TSSI-U (β=-.089, -.130,P<.05). Conclusion：It suggests that WeChat usage may be a related factor for undergraduates'upward social comparison.

Effect of Delivery Format on Student Outcomes and Perceptions of a Veterinary Medicine Course: Synchronous Versus Asynchronous Learning

The COVID-19 pandemic prompted instruction at many veterinary schools to switch to an emergency remote teaching format to prevent viral transmission associated with in-person synchronous lectures. This study surveyed student perspectives and academic performance in a pre-planned online second-year veterinary toxicology course given at North Carolina State University in Spring 2020. This course relied on asynchronous narrated presentations for content delivery. This method of delivery predated the pandemic and was used throughout the course. Academic performance and patterns of access to materials in the online course was compared with the access patterns and performance of students given classroom-based synchronous teaching in Spring 2019. Assessments evaluated in this study were identical across courses. Students’ academic performance was unaffected by delivery method. Lack of instructor interaction was an important perceived barrier in the asynchronous course. Asynchronous course materials were uniformly accessed across all days of the week, while supplemental materials for the face-to-face course showed a weekly pattern. Moving from letter grades to pass/fail did not change access frequency to supplemental course materials but led to decreased video usage in the asynchronous course. Results suggest that although some veterinary students perceived the switch in delivery format negatively, the method of delivery did not adversely affect performance in this preclinical course.

A Dynamic Scalable Security Model for Data at Rest in fog Computing Environment

Fog computing brings cloud services closer to the network’s Edge. Despite various applications in today’s world, these applications lack in data security aspects. Developers have few solutions that need to be tested thoroughly. Data encipherment is one of the most popular mechanisms to protect data confidentiality, data integrity, etc. We propose two steps flexible dynamic scalable model in which the system will dynamically choose an encryption mechanism depending on the access frequency of data being encrypted. If data is frequently accessed, then the model will choose the algorithm with minimum computational complexity. In next step, model will use a scalable approach to decide the security strength needed by determining size of encryption key. A longer key will be used to encrypt more sensitive and secretive data automatically by security model and a smaller key will be used to encrypt public data or less sensitive, saving the fog node from computation overload. Our model is more secure and dynamic in nature with scalable security strength.