Smart Education 4.0: Balancing Dual-Distance and Reskilling Revolution

On January 22, 2020, in Davos, the World Economic Forum launched Reskilling Revolution, a multistakeholder initiative aimed at providing better education, new skills, and better jobs for a billion people worldwide by 2030, so questions about improving the quality of education by introducing dual distance education and retraining of personnel at enterprises are becoming more and more relevant. In the course of the study, the authors analyzed the current problems of universities and enterprises, which allowed them to propose the creation of a smart cluster. The authors demonstrated the interaction of cluster participants using the example of education. The study showed that it is with such a tool that it is possible to solve the current problems of the participants, and in general, to improve the quality of education, the well-being of the region and, as a result, when scaling up, the well-being of the country and its image and culture on the world stage. The study showed that only through joint efforts and cooperation between universities, employers, research institutes and the state, it is possible not only to improve the quality of education but also to provide enterprises with a qualified workforce on a permanent rotation basis than to increase the competitiveness of business. This will allow research institutes and universities to raise funding, which can be directed not only to maintaining the current state but also to the development and introduction of resource-saving technologies, which we see as further research methods.

Lazy Learning Associative Classification with WkNN and DWkNN Algorithm

One of the algorithms, which prudently denote better outcomes than the traditional associative classification systems, is the Lazy learning associative classification (LLAC), where the processing of training data is delayed until a test instance is received, whereas in eager learning, before receiving queries, the system begins to process training data. Traditional method assumes that all items within a transaction is same, which is not always true. This paper recommends a new framework called lazy learning associative classification with WkNN (LLAC_WkNN) which uses weighted kNN method with LLAC, that gives a subset of rules when LLAC is applied to the dataset. In order to predict the class label of the unseen test case, the weighted kNN (WkNN) algorithm is then applied to this generated subset. This creates the enhanced accuracy of the classifier. The WkNN also gives an outlier more weight. By applying Dual Distance Weight to LLAC named as LLAC_DWkNN, this limitation of WkNN is resolved. LLAC_DWkNN gives less weightage to outliers, which improve the accuracy of the classifier, further. This algorithm has been applied to different datasets and the experiment results demonstrate that the proposed method is efficient as compared to the traditional and other existing systems.

The classification of self-orthogonal codes of length 42

Self-orthogonal codes play an important role in constructing quantum-error-correcting codes. In this paper, we prove that if quasi-symmetric 2-(41, 9, 9) design exists, then it arises from self-orthogonal and self-complementary [Formula: see text] codes with dual distance of at least 5. Moreover, we emphasize the enumeration of inequivalent doubly-even codes with the needed dual distance and an automorphism of order 7. This is found to be precisely 8.