From Music to Big Music: Listening in the Age of Big Data

2016 ◽  
Vol 26 ◽  
pp. 24-27 ◽  
Author(s):  
Marinos Koutsomichalis
Keyword(s):  
Big Data ◽  
21St Century ◽  
Music Listening ◽  
Music Databases

Following a series of technological breakthroughs and the proliferation of new, cloud-based media, listening in the 21st century has become dynamic, fragmented, interactive and distributed. Contemporary audiences are typically expected to traverse (big) music databases and, employing several overlapping interfaces, to resynthesize, rather than to merely access, content. On this construal, new ways of both experiencing and thinking about music have been laid out. This article attempts to sketch the “big music” phenomenon, discussing its genesis, outlining its implications and, finally, suggesting a typology for the classification of its carrier media.

The beginning of phygital world

2020 ◽  
Vol 10 (2) ◽  
pp. 1-4
Author(s):  
Evgeny Soloviov ◽  
Alexander Danilov
Keyword(s):  
Big Data ◽  
Learning Styles ◽  
21St Century ◽  
Digital Technology ◽  
Aviation Education ◽  
Digital World ◽  
The World ◽  
Unique Experience ◽  
Smart Data

The Phygital word itself is the combination pf physical and digital technology application.This paper will highlight the detail of phygital world and its importance, also we will discuss why its matter in the world of technology along with advantages and disadvantages.It is the concept and technology is the bridge between physical and digital world which bring unique experience to the users by providing purpose of phygital world. It is the technology used in 21st century to bring smart data as opposed to big data and mix into the broader address of array of learning styles. It can bring new experience to every sector almost like, retail, medical, aviation, education etc. to maintain some reality in today’s world which is developing technology day to day. It is a general reboot which can keep economy moving and guarantee the wellbeing of future in terms of both online and offline.

Strategic Communication in the C-Suite

2017 ◽  
Vol 54 (2) ◽  
pp. 146-160 ◽  
Author(s):  
Paul A. Argenti
Keyword(s):  
Big Data ◽  
21St Century ◽  
Elevated Level ◽  
Research Question ◽  
Strategic Communication ◽  
Central Research ◽  
Corporate Communications ◽  
Leadership Communication ◽  
The Past ◽  
Communication In Organizations

This article explores the ways in which C-suite executives are using corporate communications to execute strategy. Over the past two decades, we have seen a profound shift in how leaders view communications within organizations. This shift has moved from a tactical and superficial focus (speech writing, media placements) to a more strategic and elevated level (developing and implementing strategy through communication, sophisticated measurement using big data to understand constituencies and influence reputation). Thus, the central research question in this article is focused on the following theme: “How do leaders use communications to execute strategy in the 21st century?” Through a review of current literature on the topic and synthesis of both published and newly conducted interviews, the article provides a snapshot of leadership communication in organizations today as it relates to the execution of strategy.

scholarly journals Support Vector Machines in Big Data Classification: A Systematic Literature Review

2021 ◽  
Author(s):  
Mohammad Hassan Almaspoor ◽  
Ali Safaei ◽  
Afshin Salajegheh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Support Vector ◽  
Research Areas ◽  
Large Scale Data ◽  
Training Samples ◽  
Big Data Classification ◽  
Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

scholarly journals Evolution of the framework for 21st century competencies

2013 ◽  
pp. 10-24
Keyword(s):  
Information And Communication Technologies ◽  
21St Century ◽  
International Organizations ◽  
Conceptual Models ◽  
Communication Technologies ◽  
Educational Policies ◽  
Special Focus ◽  
Information And Communication ◽  
Ict Competencies

This article presents the successive changes and evolution of the frameworks for 21st century competencies, since the appearance of the first conceptual models during the final years of the last century, and also it is a review of the competencies that are needed in the 21st century with a special focus on the Information and Communication Technologies (ICT) competencies. The included frameworks have been elaborated by diverse institutions such as international organizations, private consortia and also governments as a guideline for educational policies in elementary and secondary schools. Later, the frameworks are compared and analyzed according to a classification of the competencies into general categories, in order to visualize some trends and obtain some insights about the direction they are heading. Finally, it provides some suggestions for the conception of future frameworks.

scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

scholarly journals Methods of big data definition: Russian and foreign experience

2021 ◽  
pp. 143-157
Author(s):  
Kseniia Antipova
Keyword(s):  
Big Data ◽  
Russian Federation ◽  
Personal Data ◽  
Legal Regulation ◽  
The European Union ◽  
Legal Doctrine ◽  
Original Definition ◽  
Data Definition ◽  
The Russian Federation

This article explores the main approaches of Russian and foreign authors towards big data definition; reflects the classification of data, components of big data; and provides comparative characteristics to legal regulation of big data. The subject of this research is the legislation of the Russian Federation and legislation of the European Union that regulate the activity on collection, processing and use of big data, personal data and information; judicial and arbitration practice of the Russian Federation in the sphere of personal data; normative legal acts of the Russian Federation; governmental regulation of the Russian Federation and foreign countries in the area of processing, use and transmission of data; as well as legal doctrine in the field of research dedicated to the nature of big data. The relevance of this research is substantiated by the fact that there is yet no conceptual uniformity with regards to big data in the world; the essence and methods of regulating big data are not fully explored. The goal of this research is determine the legal qualification of the data that comprise big data. The task lies in giving definition to the term “big data”; demonstrate the approaches towards determination of legal nature of big data; conduct  classification of big data; outline the criteria for distinguishing data that comprise the concept of big data; formulate the model for optimal regulation of relations in the process of activity on collection, processing, and use of the data. The original definition of big data in the narrow and broad sense is provided. As a result, the author distinguishes the types of data, reflects the legal qualification of data depending on the category of data contained therein: industrial data, user data, and personal data. Attention is also turned to the contractual form of big data circulation.

scholarly journals Learn from every mistake! Hierarchical information combination in astronomy

2016 ◽  
Vol 12 (S325) ◽  
pp. 39-45 ◽  
Author(s):  
Maria Süveges ◽  
Sotiria Fotopoulou ◽  
Jean Coupon ◽  
Stéphane Paltani ◽  
Laurent Eyer ◽  
...  
Keyword(s):  
Big Data ◽  
Probability Distributions ◽  
Variable Stars ◽  
Alternative Methods ◽  
Photometric Redshift ◽  
Promising Tool ◽  
Combination Methods ◽  
General Improvement ◽  
Output Probability

AbstractThroughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical “Big Data” era.

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

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