A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

Affective matches of fabric and lighting chromaticity

This study investigates the impact of lighting colors on subjective judgments of fabric: in particular, whether the influence of lighting varies depending on fabric types and color combinations. We conducted two visual assessments. In Study 1 (N = 44), eight illuminants and six types of fabric were presented as cloth stimuli. Derived from the literature review, four sets of adjectives (humble-luxurious, cool-warm, old-new, and not preferred-preferred) were used as metrics. In Study 2 (N = 41), five sets of fabric color combination swatches were assessed under lighting conditions that were identical to those of Study 1. Three bipolar scales (ordinary-characterful, classic-modern, and soft-rigid), were employed from factor analysis along with three unipolar scales (luxurious, preferred, harmonious with lighting). The results showed that hue characteristics of lighting and cloth types influenced participants' perceptions of the fabric. Overall, white lighting with 4000 K was the most preferred and luxurious lighting across various types of clothes, while a pinkish white with 4700 K (duv = −0.0127) was the best matched in every color combination. In addition, there were interaction effects between lighting colors, clothes types, and fabric color combinations with regard to each of the perceptual qualities. This study provides empirical evidence for optimally match lighting colors with fabric in the presentation of fabric goods.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Encapsulated sodium supplementation of 4 weeks does not alter salt taste preferences in a controlled low sodium and low potassium diet

Preference for saltiness is learned by oral exposure to salt taste; however, some data suggest a role for bodily sodium and potassium levels on salt taste preferences as well. The objective was to investigate whether encapsulated sodium and potassium supplementation lead to altered salt taste responses among adults with high blood pressure on a low sodium and low potassium diet. Twenty-six participants with untreated upper-range prehypertension or stage 1 hypertension were on a fully controlled low sodium and low potassium diet (both targeted at 2 g/day) for 13 weeks. Participants received capsules with sodium (3 g/d), potassium (3 g/d), or placebo, for 4 weeks each, in randomized order in a double blind crossover design. Sensory evaluation was done before and after each supplementation period and involved ratings of pleasantness and intensity in different salt (NaCl) concentrations in food and water, desire-to-eat salty food, and detection threshold for NaCl. Neither sodium supplementation nor potassium supplementation led to alterations in salt taste responses in food and water, and did not affect detection threshold (P = 0.59). There was no clear role for sodium or potassium supplementation on desire-to-eat salty food. In addition, we did not find effects of reduced oral exposure to salt over weeks, through the sodium-reduced diet, on salt taste preferences, in contrast to earlier studies. In conclusion, the results of this study suggest preference for saltiness is independent of changes in bodily sodium or potassium levels.     

Color preferences for different topics in connection to personal characteristics

Studies on color preferences are dependent on the topic and the relationships with personal characteristics, particularly personality, but these are seldom studied in one population. Therefore a questionnaire was collected from 1095 Dutch people asking for color preferences about different topics and relating them to personal characteristics. Color preferences regarding different topics show different patterns and significant differences were found between gender, age, education and personality such as being technical, being emotional or being a team player. Also, different colors were mentioned when asked for colors that stimulate to be quiet, energetic, and able to focus or creative. Probably, due to unconsciousness of contexts, many people had no color preference, a result that in the literature seldom is mentioned. Blue was the overall favorite color; however, most males chose for blue (25%) while most females had no color preference (18%). Black was the overall favorite color for clothing, mainly chosen by females (40%), while males primarily chose blue (27%). For building interiors subjects preferred white. For moods, subjects preferred white for being quiet or being able to focus, red for being energetic and had no color preference for being creative. It is concluded that color preferences are dependent upon the topic, and personal characteristics. The findings are important for architects, interior designers, fashion designers and product designers to have a basic idea of preferred colors for different objects by different types of people. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 62–71, 2015     

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.