The Implementation of Multisensory Technique for Children with Dyslexia

Children with learning disabilities are a children's physiological or biological condition in which the competence or achievement is not according to predetermined standard criteria—learning disabilities in the form of errors in reading called dyslexia. Children with specific learning difficulties dyslexia experience difficulties in academic aspects; therefore, it is necessary to carry out an academic assessment and material for tutors to improve dyslexia reading skills. The multisensory technique is alternatives that used as reference material for tutors to improve dyslexia reading skills. Multisensory Techniques that can be used include 1) Reading and Spelling Training; 2) Visual Technique; 3) Auditory Technique, and 4) Tactile Technique. The reading and spelling focus on maintaining relationships between sounds and symbols starts with a single letter and continues with consonant combinations, vowel continuation, and complex letter groupings. The Visual Technique can start by using a picture card with the word written on the bottom (flashcard). Auditory technique for children who have difficulty with sound problems, teach a pair of short words and ask the child to say which word is correct. In addition, children with dyslexia will have the best learning by touch, so it is essential to incorporate this learning style into the instruction as a tactile technique.

An enhanced cosine-based visual technique for the robust tweets data clustering

PurposeThe purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results.Design/methodology/approachLet “N” be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of N documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here N = 5) and corresponding documents are defined in projected space with five viewpoints, say, v1,v2, v3, v4, and v5. For example, similarity features between two documents (viewpoints v1, and v2) are computed concerning the other three multi-viewpoints (v3, v4, and v5), unlike a single viewpoint in traditional cosine metric.FindingsHealthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF–IDF) for unlabelled tweets.Originality/valueTopic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.

Animating objectivity: a Chicago settlement’s use of numeric and aesthetic knowledges to render its immigrant neighbours and neighbourhood knowable

This chapter presents findings from archival research and secondary sources from the Chicago Settlement. It argues that it was the aesthetic analysis of the slum that was drawn upon for positioning the evidentiary value of maps. The author suggests that the reformers’ objective in overlaying a numerically based visual technique of calculation with an evocative description of the smells and sounds of the immigrant ‘slum;’ was to permit the assumed public an opportunity to sense the imperative for legal and institutional change necessary for urban renewal.

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.

Intelligent Visual Similarity-Based Phishing Websites Detection

This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.

Customer complaint journey mapping: a qualitative approach

PurposeThis paper aims to extend the customer complaint behaviour (CCB) knowledge by introducing a visual technique called customer complaint journey mapping as a means of capturing and understanding multi-faceted service failures involving multiple actors.Design/methodology/approachResearch participants were trained to record contemporaneous accounts of future dissatisfactory dining experiences. Minimising issues of memory recall whilst faithfully capturing complainants' raw emotions. These recordings formed the basis for follow up interviews, based on the critical incident technique.FindingsThe central finding of this paper was how other actors outside of the traditional service dyad played a dynamic role in co-creating a complainants' emotions and subsequent behaviours.Practical implicationsThe resulting customer complaint maps give deep insights into the complex social dynamics involved in CCB, providing a powerful tool for both researchers and staff responsible for recovery strategies.Originality/valueThe mapping framework provides an innovative means of capturing the actual complaint experiences of customers and the role of other actors, utilising a multi-method approach designed to address various limitations of existing CCB research.

Graphic geography: drawing territories at the Po Delta (Italy)

Geography and the graphic image have a long, intertwined history of exchange. In recent scholarship, the graphic image plays an important role in geography’s creative (re)turn and geographers are experimenting with new visual languages and creative practices to carry out research and communicate with wider audiences. This paper explores geography as a ‘graphic’ discipline that represents and produces spatial knowledge by experimenting with scribing, a verbo-visual technique. In the first part of the article, we propose an auto-ethnographic account of a residential seminar with students in Local and Sustainable Territorial Development, held in 2017 at the Po Delta (Italy), where we experimented with scribing as a tool for geographical fieldwork and spatial storytelling, understanding it as a practice for seeing beyond representing territories. The second more theoretical part of the paper presents scribing as a means to respond to the increasing need for more creative visualisation tools in qualitative research and highlights the performative potential of scribing as a practice/product for thinking about space. The graphic product of scribing results from an intersubjective dialogue and is used to develop spatial analyses and disseminate geographical research beyond academic boundaries, engaging non-expert audiences and local communities.