Tourism Souvenir Ceramics Craft Creation: Design Inspiration of Malang Local Artefact Culture

Crafts should be developed as part of the tourism sector, because when this pandemic ends, the tourism sector will need to rise up and be ready to answer the challenge. The tourism industry should be supported by souvenir crafts, but the products lack the design variance that is reflected in the local cultural character. This research aimed to develop designs based on local culture. The methods included studying local culture, formulating local culture concepts as Malang souvenir ceramic creation ideas, doing creative shape exploration and technique exploration, creating art and product prototypes, testing and validating the product diversification in terms of concept and prototype, and lastly, improving the product and analyzing the creative results. Keywords: Malang souvenir ceramics, design diversification

Narration on ethnic jewellery of Kerala-focusing on design, inspiration and morphology of motifs

Artefacts in the form of Jewellery reflect the essence of the lifestyle of the people who create and wear them, both in the historic past and in the living present. They act as the connecting link between our ancestors, our traditions, and our history. Jewellery is used--both in the past and the present-- to express the social status of the wearer, to mark tribal identity, and to serve as amulets for protection from harm. This paper portrays the ethnic ornaments of Kerala with insights gained from examples of Jewellery conserved in the Hill Palace Museum and Kerala Folklore Museum, in Cochin, Kerala. Included are Thurai Balibandham, Gaurisankara Mala, Veera Srunkhala, Oddyanam, Bead necklaces, Nagapadathali and Temple Jewellery. Whenever possible, traditional Jewellery is compared with modern examples to illustrate how--though streamlined, traditional designs are still a living element in the Jewellery of Kerala today.

Development of a Collection of Garments Inspired by the Hawa Mahal Historical Monument

Sources of inspiration play a vital role during the initial stages of the fashion design process by providing a specific direction to the entire fashion design process. Fashion designers interpret their imagination to improve the creative use of design inspirations during the development of clothing collections. Such exploration for design inspiration is crucial in the fashion design process for absorbing visual ideas and translating them into original creative clothing. The uniqueness of such creatively and systematically designed original clothing will also improve significantly. The current study focuses on the systematic development of a collection of casual women’s wear inspired by the Hawa Mahal (The Palace of Winds) historical monument in Jaipur, Rajasthan. The fabric patterns were developed by extracting motifs from the Hawa Mahal architectural marvel using computer-aided designing solutions and digital printing with hand embroidery. In order to check the market potential of developed garments, a mini-survey was also conducted to analyse the extent of the appropriateness of garment silhouettes, fitting and drape, and overall aesthetic features among targeted consumers.

Double attention recurrent convolution neural network for answer selection

Answer selection is one of the key steps in many question answering (QA) applications. In this paper, a new deep model with two kinds of attention is proposed for answer selection: the double attention recurrent convolution neural network (DARCNN). Double attention means self-attention and cross-attention. The design inspiration of this model came from the transformer in the domain of machine translation. Self-attention can directly calculate dependencies between words regardless of the distance. However, self-attention ignores the distinction between its surrounding words and other words. Thus, we design a decay self-attention that prioritizes local words in a sentence. In addition, cross-attention is established to achieve interaction between question and candidate answer. With the outputs of self-attention and decay self-attention, we can get two kinds of interactive information via cross-attention. Finally, using the feature vectors of the question and answer, elementwise multiplication is used to combine with them and multilayer perceptron is used to predict the matching score. Experimental results on four QA datasets containing Chinese and English show that DARCNN performs better than other answer selection models, thereby demonstrating the effectiveness of self-attention, decay self-attention and cross-attention in answer selection tasks.