Master Algorithms Research Articles

Changing the Paradigm of Traditional Journalism to Digital Journalism: Impact on Professionalism and Journalism Credibility

The development of information technology and the internet has significantly transformed the landscape of media and journalism. Traditional journalism, which relied on print and electronic broadcasting, has evolved into digital journalism supported by online platforms. This change has had a profound impact on the professionalism and credibility of journalists. This research aims to analyze the paradigm shift from traditional journalism to digital journalism and explore its impact on journalistic professionalism and credibility. The methods used in this study are literature review and comparative analysis. In the literature review, we examine the relevant literature on the paradigm shift in journalism and its impact on journalistic professionalism and credibility. Comparative analysis is carried out by comparing the characteristics of traditional journalism and digital journalism and analyzing the changes that have occurred. The results of the study indicate that the paradigm shift from traditional journalism to digital journalism has changed the role of journalists in gathering, editing and delivering news. Digital journalism provides greater freedom and flexibility for journalists in creating content, but it also brings new challenges related to the validity and credibility of information. Journalists today are faced with demands to adapt to new technologies, master algorithms, and build a strong online presence. The results of the study indicate that the paradigm shift from traditional journalism to digital journalism has changed the role of journalists in gathering, editing and delivering news. Digital journalism provides greater freedom and flexibility for journalists in creating content, but it also brings new challenges related to the validity and credibility of information. Journalists today are faced with demands to adapt to new technologies, master algorithms, and build a strong online presence.

Synchronization conditions and Real-time constraints in co-simulation and Hardware-in-the-Loop techniques for Cyber–Physical Energy System assessment

Abstract Co-simulation and Hardware-in-the-Loop, as well as the integration of Hardware-in-the-Loop to a co-simulation framework are innovative approaches that can get a step closer to a complete and realistic assessment and validation of cyber–Physical energy system (i.e. smart grid). While the coupling techniques and the master algorithms ensure good functionality of the experiment, the whole framework has to follow a rigorous time semantic so that the results would be accurate and meaningful. In this paper, we present a formalization of the synchronization aspect in co-simulation and Hardware-in-the-Loop. Using this formulation, we propose synchronization and real-time conditions under which a co-simulation/Hardware-in-the-Loop framework is well-posed and correctly synchronized. Moreover, the classical real-time constraint in Hardware-in-the-Loop is relaxed for more flexible application. The proposed formulation is illustrated via an cross-infrastructure implementation of secondary control in micro-grid, with integration of remote controllers to real-time co-simulation framework.

ИНТЕЛЛЕКТУАЛЬНЫЕ УМЕНИЯ ШКОЛЬНИКОВ: СУЩНОСТЬ, ОЦЕНКА И МЕТОДЫ РАЗВИТИЯ НА ЗАНЯТИЯХ МУЗЫКОЙ

The article substantiates the advisability of developing modern schoolchildren’s intellectual skills during piano lessons. It is shown that intellectual skills are a multicomponent phenomenon acting as a structural component of thinking and mental development. The author gives the results of the research of intellectual activity conducted at the Children’s Music School in Dubna that show both the high level of intelligence among the students of music school and confident decrease in motivation for cognitive activity with the age increase among 6-9-year-old children. The assumption is substantiated that intellectual skills development in the field of musical performance can give positive results for general educational skills development. The ideas set forth in this paper can be used in the organization of training process in educational institutions of various types and are recommended for teaching schoolchildren, who perceive information in a complex way, in the form of clip clusters, master algorithms and distinguish details poorly.

VRKSHA: a novel tree structure for time-profiled temporal association mining

Mining association patterns from a time-stamped temporal database distributed over finite time slots is implicitly associated with task of scanning the input database. Finding supports of itemsets requires scanning the input database. Database scan can be performed in either snapshot or lattice-based approach. Sequential and SPAMINE methods for similarity-profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar are based on the snapshot database scan and lattice scan, respectively. Snapshot database scan involves scanning multi-time slot database time slot by time slot. The major limitation of Sequential method is the requirement to retain original temporal database in the disk for finding itemset support computations. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in the memory and also eliminates the need to retain database in memory. The basic idea is to generate a compressed time-stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference w.r.t each time slot and validating whether the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed Sequential approach is from the fact that it is a single database scan approach excluding the initial database scan performed for computing true supports of singleton items. VRKSHA overcomes the major limitation of retaining database in memory that is required by SPAMINE, G-SPAMINE, MASTER algorithms. Experiment results prove that computational time and memory consumed by VRKSHA are significantly very much better than by approaches such as Naive, Sequential, SPAMINE, and G-SPAMINE. To the best of our survey and knowledge, VRKSHA is the pioneering work to introduce and propose a compressed tree-based data structure for mining similarity-profiled temporal association patterns in the area of time-profiled temporal association mining.

Combining Online Algorithms for Acceptance and Rejection

Resource allocation and admission control are critical tasks in a communication network that often must be performed online. Algorithms for these types of problems have been considered both under benefit models (e.g., with a goal of approximately maximizing the number of requests accepted) and under cost models (e.g., with a goal of approximately minimizing the number of requests rejected). Unfortunately, algorithms designed for these two measures can often be quite different, even polar opposites. In this work we consider the problem of combining algorithms designed for each of these objectives in a way that is good under both measures simultaneously. More formally, we are given an algorithm $A$ that is $c_A$ competitive with respect to the number of accepted requests and an algorithm $R$ that is $c_R$ competitive with respect to the number of rejected requests. We show how to derive a combined algorithm with competitive ratio $O(c_R c_A)$ for rejection and $O(c_A)$ for acceptance. We also build on known techniques to show that given a collection of $k$ algorithms, we can construct one master algorithm that performs similarly to the best algorithm among the $k$ for the acceptance problem and another master algorithm that performs similarly to the best algorithm among the $k$ for the rejection problem. Using our main result we can combine the two master algorithms to a single algorithm that guarantees both rejection and acceptance competitiveness.

Learning nested differences of intersection-closed concept classes

This paper introduces a new framework for constructing learning algorithms. Our methods involve master algorithms which use learning algorithms for intersection-closed concept classes as subroutines. For example, we give a master algorithm capable of learning any concept class whose members can be expressed as nested differences (for example, c 1 − (c 2 − (c 3 − (c 4 − c 5)))) of concepts from an intersection-closed class. We show that our algorithms are optimal or nearly optimal with respect to several different criteria. These criteria include: the number of examples needed to produce a good hypothesis with high confidence, the worst case total number of mistakes made, and the expected number of mistakes made in the firstt trials.

Learning Nested Differences of Intersection-Closed Concept Classes

This paper introduces a new framework for constructing learning algorithms. Our methods involve master algorithms which use learning algorithms for intersection-closed concept classes as subroutines. For example, we give a master algorithm capable of learning any concept class whose members can be expressed as nested differences (for example, c 1 – (c 2 – (c 3 – (c 4 – c 5)))) of concepts from an intersection-closed class. We show that our algorithms are optimal or nearly optimal with respect to several different criteria. These criteria include: the number of examples needed to produce a good hypothesis with high confidence, the worst case total number of mistakes made, and the expected number of mistakes made in the first t trials.