Islamic Arts Museum, Malaysia: Educational Tool for Reviving Architectural Heritage

The Islamic art and architecture have played a significant role in the development of historic cities in the Muslim world; they were developed through time in response to socioeconomic and cultural needs of the society. The paper will focus on the experience of the Islamic Arts Museum in terms of its role in raising public awareness about Islamic art and architecture through its building that combines modernity and heritage in unique Islamic architectural style and educational programs and activities that educate people about conservation of Islamic heritage. The aim of this essay is to present the experience of IAMM in promoting Islamic art and architecture in order to share experience as a successful model. To achieve this aim the existing conditions of this museum were examined in terms of its building, decorated element, cultural activities and conservation programs using traditional ways and modern technologies. A broad range of information was collected from various sources and through a field survey carried out in the selected museum from modern country leading development in the Muslim world. The collected information was analyzed with particular regard to the special character. This paper is an attempt to address the important issues of educational programs that raise public awareness about heritage through interior design and display of artifact from different regions of Muslim world matters that have been raised in many museums around the world and it is hoped that it is going to be a significant contribution to the subject of reviving Islamic architecture in the modern world

Sino-arabic Script and Architectural Inscriptions in Xi\u27an Great Mosque, China

The Islamic art have played a significant role in the development of Muslim Chinese community in China, it wasdeveloped through time in response to cultural needs of the minority Muslim groups in China. The Islamiccalligraphy was widely used in architecture, especially in interior and exterior decoration of mosques andother religious buildings. The aim of this study is to interpret the Islamic art and architecture in China throughapplication of Sino-Arabic script on mosques and crafts produced by Muslim minority in China in relation to theIslamic civilization and Chinese civilization, in order to suggest some guidelines for the preservation of thisforgotten Islamic heritage. To achieve the aim of this paper the Sino-Arabic inscriptions will be examined inorder to determine their characteristics and the nature of the effects to which they have been subjected. Abroad range of information was collected from various sources and through a field survey that was carried outin Xi\u27an Great Mosque in China. The collected information from field work will be analyzed with particularregard to the special character of Chinese Islamic art and architecture. This study is an attempt to addressthe important topic of Islamic calligraphy and its application on architectural buildings in China as part ofissues of Islamic architectural heritage and its integration with local tradition that have been occurred in theMuslim world and it is hoped that it is going to be a significant contribution to the subject of Islamic art andarchitecture in China as well as revival and preservation of this forgotten heritage. Detailed conclusion will bearrived at the end and specific suggestions are intended to assist in examining the topic in depth and helping indeveloping guidelines for regional expansion and adaptation of Islamic art and architecture with localenvironmental condition to contribute more for the future of Muslim heritage and civilization

Context Normalization Layer with Applications

International audienceDeep neural networks (DNNs) have gained prominence in many areas such as computer vision (CV), natural language processing (NLP), robotics, and bioinformatics. Whiletheir deep and complex structure enables powerful representation and hierarchical learning, it poses serious challenges (e.g., internal covariate shift, vanishing/exploding gradients, overfitting, and computational complexity), during their training phase.Neuron activity normalization is an effective strategy that lives up to these challenges. This procedure consists in promoting stability, creating a balanced learning, improving performance generalization and gradient flow efficiency. Traditional normalization methods often overlook inherent dataset relationships. For example, batch normalization (BN) estimates mean and standard deviation from randomly constructed mini-batches (composed of unrelated samples), leading to performance dependence solelyon the size of mini-batches, without accounting for data correlation within these batches. Conventional techniques such as Layer Normalization, Instance Normalization, and Group Normalization estimate normalization parameters per instance, addressing mini-batch size issues. Mixture Normalization (MN) utilizes a two-step process: (i) training a Gaussian mixture model (GMM) to determine components parameters, and (ii)normalizing activations accordingly. MN outperforms BN but incurs computational overhead due to GMM usage. To overcome these limitations, we propose a novel methodology that we named ”Context Normalization” (CN). Our approach assumes that the data distribution can be represented as a mixture of Gaussian components. However, unlike MN that assumes a-priori that data are partitioned with respect to a set of Gaussian distributions, CN introduces the notion of concept that accounts for datarelationship via a neural network classification scheme. Samples that are gathered within a cluster define a context. The estimation of the Gaussian components parameters is conducted through a supervised neural network-based concept classification. CN ismore precise when clusters are thick and not sparse. Extensive comparative experiments conducted on various datasets demonstrates the superiority of CN over BN and MN in terms of convergence speed and performance generalization. In fact, CNoutperforms BN and MN with a convergence speed margin of 5% and a performance margin of 10%. These results reveal the importance and the need of capturing inherent data context to learn the Gaussian component parameters. Our proposed approach harnesses data relationships, and therefore enhances deep learning models in various applications

Context Normalization Layer with Applications

International audienc