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Arts organizations across the country are actively expanding their efforts to increase public participation in their programs. This report presents the findings of a RAND study sponsored by the Wallace-Reader's Digest Funds that looks at the process by which individuals become involved in the arts and attempts to identify ways in which arts institutions can most effectively influence this process. The report presents a behavioral model that identifies the main factors influencing individual decisions about the arts, based on site visits to institutions that have been particularly successful in attracting participants to their programs and in-depth interviews with the directors of more than 100 institutions that have received grants from the Wallace-Reader's Digest Funds and the Knight Foundation to encourage greater involvement in the arts. The model and a set of guidelines to help institutions approach the task of participation building constitute a framework that can assist in devising participation-building approaches that fit with an institution's overall purpose and mission, its available resources, and the community environment in which it operates--in other words, a framework that will enable arts institutions to take an integrative approach to building participation in the arts.

The academic investigation of Tibetan rock art began in 1985. To date, rock paintings have been discovered in nearly 20 counties or cities in the Tibet Autonomous Region, including 150 rock art sites containing more than 1000 individual panels and nearly 10 000 single motifs. In parallel with these discoveries, domestic and foreign research efforts in Tibetan rock art study have increased significantly, yielding impressive results. Consequently, not only has it formed its own theory but has also established its unique research method.

Fuzzy sets extend deterministic multi-criteria decision-making (MCDM) methods to deal with uncertainty and imprecision in decision making. Over the years, many generalizations have been proposed to the classical Fuzzy sets to deal with different kinds of imprecise and subjective data. One such generalization is Atanassov’s Intuitionistic Fuzzy Set (IFS) which is becoming increasingly popular in MCDM research. Together, the two notions of uncertainty modeling: ‘classical fuzzy set’ (Zadeh) and intuitionistic fuzzy set (Atanassov) have been utilized in many real-world MCDM applications spanning diverse disciplines. As IFS grows in popularity by the day, this paper conducts a literature survey to (1) compare the trend of publications of ‘classical fuzzy’ set theory and its generalized form, the intuitionistic fuzzy set (IFS) as used in MCDM methods from 2000 to 2015; (2) classify their contributions into three novel tracks of applications , hybrid , and extended approaches; (3) determine which MCDM method is the most used together with the two forms of fuzzy modeling; and (4) report on other measures such as leading authors and their country affiliations, yearly scholarly contributions, and the subject areas where most of the two fuzzy notions in MCDM approaches are applied. Finally, the study presents trends and directions as far as the applications of classical fuzzy set and intuitionistic fuzzy sets in MCDM are concerned.

Ildiko Kovacs's major survey exhibition 'The DNA of Colour' traces the last ten years of the artist's prolific practice, canvassing her rich explorations of the possibilities of paint through rhythmic abstraction. Curated by Sioux Garside for Orange Regional Gallery and Canberra's Drill Hall Gallery, the exhibition focuses on Kovacs's iconic roller paintings as well as her most recent angular line works. The breadth of exploration showcases the vital spirit that is omnipresent in Kovacs's oeuvre, highlighting her important contribution to contemporary Australian abstraction.

Since 2011, when the concepts of Industry 4.0 were first announced, this industrial revolution has grown and expanded from some theoretical concepts to real-world applications. Its practicalities can be found in many fields and affect nearly all of us in so many ways. While we are adapting to new changes, adjustments are starting to reveal on national and international levels. It is becoming clear that it is not just new innovations at play, technical advancements, governmental policies and markets have never been so intertwined. Here, we generally describe the concepts of Industry 4.0, explain some new terminologies and challenges for clarity and completeness. The key of this paper is that we summarise over 14 countries’ up-to-date national strategies and plans for Industry 4.0. Some of them are bottom-up, such as Portugal, some top-down, such as Italy, a few like the United States had already been moving in this direction long before 2011. We see governments are tailoring their efforts accordingly, and industries are adapting as well as driving those changes.

As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase the cost of data storage and computing; it also influences the efficiency and accuracy of dealing with problems. Feature dimensionality reduction as a key link in the process of pattern recognition has become one hot and difficulty spot in the field of pattern recognition, machine learning and data mining. It is one of the most challenging research fields, which has been favored by most of the scholars’ attention. How to implement “low loss” in the process of feature dimension reduction, keep the nature of the original data, find out the best mapping and get the optimal low dimensional data are the keys aims of the research. In this paper, two-dimensionality reduction methods, feature selection and feature extraction, are introduced; the current mainstream dimensionality reduction algorithms are analyzed, including the method for small sample and method based on deep learning. For each algorithm, examples of their application are given and the advantages and disadvantages of these methods are evaluated.

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.

Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore ® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.