ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis

Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, "ABANICCO" (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC-NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO's accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.This research was funded by the Ministerio de Ciencia, Innovacción y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB, MCIN/AEI/10.13039/501100011033 co-financed by the European Regional Development Fund (ERDF) "A way of making Europe" to A.M.-B. and L.N.-S.Publicad

Key words for learning science: the structure of students' sign networks in mechanics

Learning science requires conceptual change. Hence, understanding this process is important to develop successful methods of science teaching. Susan Carey’s Theory of the Origin of Concepts (TOOC) is one of the most complete theories of conceptual change. TOOC identifies Quinian bootstrapping as a mechanism that drives conceptual development, with a network of connections between signs (words) forming a placeholder sign structure, which then guides the development of concepts. The nature of placeholder sign structure within scientific domains of knowledge is not known. This research has examined sign networks in mechanics formed by 6 A-level Physics students learning mechanics and has contrasted these networks with the networks of 5 A-level Psychology students (all aged 16 to 18). Considered word associations and snowball sampling were used to measure individual directed sign networks. The structure of these networks was characterised using basic network parameters and by fitting the degree distributions with different models (e.g., Poisson distribution or power-law) to determine whether the networks contained highly connected hub-words. The Force Conceptual Inventory (FCI) and interviews were used to assess participants’ conceptual understanding of mechanics. The interview transcripts were analysed using content and lexicogrammatical analysis. The FCI scores and the interview analysis showed the physicists and psychologists have a different conceptual understanding of mechanics, but that the macrostructure of all the placeholder sign networks is similar. The networks are sparse, have short average path lengths and low average degree, and high clustering. However, the connections (microstructure) between signs are different in the physics and psychology networks. These structural features suggest conceptual development is guided by a small number of connections (sparseness and low average degree) between neighbouring signs (clusters), which are closely connected to other parts of the network (short average path length). All the sign networks contain hub-words, with, force, energy, time, and particle occurring frequently in all networks, but mass is only found in the physics networks. The presence of hub-words suggests the clusters of signs are connected via these hub-words and that the sign structure ‘grows’, or is organised, around hub-words. Furthermore, the interview analysis of the use of force in explanations and the FCI scores suggest this structural feature is present in the psychology networks before these participants have a full conceptual understanding of mechanics (i.e., force is recognised as a cause of motion, rather than acceleration). This is consistent with TOOC account of Quinian bootstrapping driving conceptual development and has implications for the pedagogical practice in physics and beyond

A survey on adaptive random testing

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work

An exploration of process oriented guided inquiry learning in undergraduate Chemistry classes

The research study explored student’s understanding of stereochemistry and their perceptions of learning chemistry in first year undergraduate chemistry classes following a modified Process-Oriented Guided Inquiry Learning (POGIL) that included group work. A 5-item two-tier stereochemistry concept diagnostic test (SCDT) was developed and administered to explore their understanding of stereochemistry concepts. The students’ perception of POGIL learning was gauged in an effort to establish construct and convergent validity to the SALG instrument

Google Glass App for Displaying ASL Videos for Deaf Children – The Preliminary Race

Glass Vision 3D is a grant-funded project focused on the goal of developing and researching the feasibility & usability of a Google Glass app that will allow young Deaf children to look at an object in the classroom and see an augmented reality projection that displays an American Sign Language (ASL) related video. Session will show the system (Glass app) that was developed and summarize feedback gathered during focus-group testing of the prototype

A survey on the application of deep learning for code injection detection

Abstract Code injection is one of the top cyber security attack vectors in the modern world. To overcome the limitations of conventional signature-based detection techniques, and to complement them when appropriate, multiple machine learning approaches have been proposed. While analysing these approaches, the surveys focus predominantly on the general intrusion detection, which can be further applied to specific vulnerabilities. In addition, among the machine learning steps, data preprocessing, being highly critical in the data analysis process, appears to be the least researched in the context of Network Intrusion Detection, namely in code injection. The goal of this survey is to fill in the gap through analysing and classifying the existing machine learning techniques applied to the code injection attack detection, with special attention to Deep Learning. Our analysis reveals that the way the input data is preprocessed considerably impacts the performance and attack detection rate. The proposed full preprocessing cycle demonstrates how various machine-learning-based approaches for detection of code injection attacks take advantage of different input data preprocessing techniques. The most used machine learning methods and preprocessing stages have been also identified

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version