Icons

Categorical orthodoxy has it that collections of ordinary mathematical structures such as groups, rings, or spaces, form categories (such as the category of groups); collections of 1-dimensional categorical structures, such as categories, monoidal categories, or categories with finite limits, form 2-categories; and collections of 2-dimensional categorical structures, such as 2-categories or bicategories, form 3-categories. We describe a useful way in which to regard bicategories as objects of a 2-category. This is a bit surprising both for technical and for conceptual reasons. The 2-cells of this 2-category are the crucial new ingredient; they are the icons of the title. These can be thought of as ``the oplax natural transformations whose components are identities'', but we shall also give a more elementary description. We describe some properties of these icons, and give applications to monoidal categories, to 2-nerves of bicategories, to 2-dimensional Lawvere theories, and to bundles of bicategories.Comment: 23 page

Recognition of animated icons by elementary‐aged children

This paper describes a study to investigate the recognizability of and preference for animated icons by elementary‐aged aged children. Fourteen typical computer‐related tasks (e.g., copy, move) were viewed by 60 school‐children in two iconic formats: animated and static. The content of the icons and the computer process or action they mimicked were drawn from a previous study in which a similar group of children was asked to depict gesturally their interpretation of the 14 tasks. Results indicated that the animated version of the icons was more recognizable and that the children greatly preferred the animated icons over the static icons. Implications for the design of enhanced user‐interfaces for children are noted

Testing of a novel web browser interface for the Chinese market

This paper compares the perspicacity, appropriateness and preference of web browser icons from leading software providers with those of a culture-specific design. This online study was conducted in Taiwan and involved 103 participants, who were given three sets of web browser icons to review, namely Microsoft Internet Explorer, Macintosh Safari, and culturally specific icons created using the Culture-Centred Design methodology. The findings of the study show that all three sets have generally high recognition rates, but that some icon functions (e.g. Go/Visit and Favourite) in all three sets have poor recognition rates and are considered inappropriate

A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.Comment: Full version. IEEE MIPR 201

Non-Clinical Benefits of Evidence - Based Veterinary Medicine

<div><strong>Clinical bottom line</strong></div><ul><li>There are few studies addressing business benefits of EBVM.</li><li>While the need for a wider adoption of EBVM has been highlighted and linked to commercial benefits, further empirical studies are needed to identify and quantify such linkages.</li></ul><p><br /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/oa-icon.jpg" alt="Open Access" /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/pr-icon.jpg" alt="Peer Reviewed" /></p

An iconic programming language for sensor-based robots

In this paper we describe an iconic programming language called Onika for sensor-based robotic systems. Onika is both modular and reconfigurable and can be used with any system architecture and real-time operating system. Onika is also a multi-level programming environment wherein tasks are built by connecting a series of icons which, in turn, can be defined in terms of other icons at the lower levels. Expert users are also allowed to use control block form to define servo tasks. The icons in Onika are both shape and color coded, like the pieces of a jigsaw puzzle, thus providing a form of error control in the development of high level applications

Book Review: The Experience of God: Icons of the Mystery

A review of The Experience of God: Icons of the Mystery by Raimon Panikkar

An Efficient hardware implementation of the tate pairing in characteristic three

DL systems with bilinear structure recently became an important base for cryptographic protocols such as identity-based encryption (IBE). Since the main computational task is the evaluation of the bilinear pairings over elliptic curves, known to be prohibitively expensive, efficient implementations are required to render them applicable in real life scenarios. We present an efficient accelerator for computing the Tate Pairing in characteristic 3, using the Modified Duursma-Lee algorithm. Our accelerator shows that it is possible to improve the area-time product by 12 times on FPGA, compared to estimated values from one of the best known hardware architecture [6] implemented on the same type of FPGA. Also the computation time is improved upto 16 times compared to software applications reported in [17]. In addition, we present the result of an ASIC implementation of the algorithm, which is the first hitherto