Stemming Hausa text: using affix-stripping rules and reference look-up

Stemming is a process of reducing a derivational or inflectional word to its root or stem by stripping all its affixes. It is been used in applications such as information retrieval, machine translation, and text summarization, as their pre-processing step to increase efficiency. Currently, there are a few stemming algorithms which have been developed for languages such as English, Arabic, Turkish, Malay and Amharic. Unfortunately, no algorithm has been used to stem text in Hausa, a Chadic language spoken in West Africa. To address this need, we propose stemming Hausa text using affix-stripping rules and reference lookup. We stemmed Hausa text, using 78 affix stripping rules applied in 4 steps and a reference look-up consisting of 1500 Hausa root words. The over-stemming index, under-stemming index, stemmer weight, word stemmed factor, correctly stemmed words factor and average words conflation factor were calculated to determine the effect of reference look-up on the strength and accuracy of the stemmer. It was observed that reference look-up aided in reducing both over-stemming and under-stemming errors, increased accuracy and has a tendency to reduce the strength of an affix stripping stemmer. The rationality behind the approach used is discussed and directions for future research are identified

Designing mobile training content: challenges and open issues

Mobile training is an evolution of electronic training and is based on mobile learning technology, which is used to design mobile learning courses. Due to the widespread deployment of mobile devices and the need to remain current with developments in mobile technology, it is important to consider the design of appropriate mobile training content to increase learners’ engagement in mobile learning courses. However, studies have emphasized the challenges in this area. Therefore, we conducted a systematic mapping study that offers an overview of the current literature in this domain based on a thorough search of the literature by using a process of selection that involves criteria for inclusion and exclusion, data extraction and synthesis strategies. Of the 194 journal articles identified in the initial search stage, 58 were selected as primary studies; they were published between 2009 and 2019. We applied a classification scheme to answer our research questions. Our study examines the current challenges in the design of mobile training content, identifies the key open issues, determines the trends in publication and emphasizes the most widely researched topics in recent years related to the design of mobile training content. Our study identifies the existing challenges and suggests further work on key open issues. Our study also suggests that, considering the major issues related to pedagogical challenges, the research focus should shift toward the design of attractive, interactive and motivating mobile content that is based on a theoretical framework for mobile training courses, and other technological and managerial challenges that can be addressed should be investigated in order to overcome the existing difficulties in the design of mobile training content and to provide better solutions for the continuity of this research domain

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region's boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region's boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods. © 2019 by the authors

Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches

With the emergence of cloud-based technology, personalized learning mechanism has increasingly become a fundamental requirement for most learning systems. This study aimed to identify the key factors that influence user adoption of cloud-based collaborative learning technology in the educational context. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), personalization construct was linked to the behavioral intention, performance expectancy and effort expectancy. This research applied a new methodological approach combining both Fuzzy Analytic Hierarchy Process (FAHP) and Structural Equation Modelling (SEM) to determine the relative weight and importance of the factors as well as to test the proposed hypotheses in the research model. Using a survey questionnaire, data was collected from 150 students of four Malaysian public universities. The findings of FAHP demonstrated that performance expectancy, social influence, and personalization were the most important factors predicting behavioral intention to adopt cloud-based collaborative learning technology from experts’ point of view. The results of the SEM showed that users’ behavioral intention was significantly influenced by performance expectancy, effort expectancy, social influence and personalization. Although, personalization performed a direct influence on behavioral intention, its indirect influence through performance expectancy and effort expectancy was also considerable. This study and its findings can serve as a baseline by which cloud service providers, ministry of education, and educational institutions can make strategic and strong decisions about adoption of cloud-based technology in educational environments

Affective computing in education: A systematic review and future research

It is becoming a trend to apply an emotional lens and to position emotions as central to educational interactions. Recently, affective computing has been one of the most actively research topics in education, attracting much attention from both academics and practitioners. However, despite the increasing number of papers published, there still are deficiencies and gaps in the comprehensive literature review in the specific area of affective computing in education. Therefore, this study presents a review of the literature on affective computing in education by selecting articles published from 2010 to 2017. A review protocol consisting of both automatic and manual searches is used to ensure the retrieval of all relevant studies. The final 94 selected papers are reviewed and relevant information extracted based on a set of research questions. This study classifies selected articles according to the research purposes, learning domains, channels and methods of affective recognition and expression, and emotion theories/models as well as the emotional states. The findings show the increased number and importance of affective computing studies in education domain in recent years. The research purposes of most affective computing studies are found to be designing emotion recognition and expression systems/methods/instruments as well as examining the relationships among emotion, motivation, learning style, and cognition. Affective measurement channels are classified into textual, visual, vocal, physiological, and multimodal channels, while the textual channel is recognized as the most widely-used affective measurement channel. Meanwhile, integration of textual and visual channels is the most widely-used multimodal channel in affective computing studies. Dimensional theories/models are the most preferred models for description of emotional states. Boredom, anger, anxiety, enjoyment, surprise, sadness, frustration, pride, hopefulness, hopelessness, shame, confusion, happiness, natural emotion, fear, joy, disgust, interest, relief, and excitement are reported as the top 20 emotional states in education domain. Finally, this study provides recommendations for future research directions to help researchers, policymakers and practitioners in the education sector to apply affective computing technology more effectively and to expand educational practices. © 2019 Elsevier Lt

River segmentation using satellite image contextual information and Bayesian classifier

Satellite-based remote sensing imaging can provide continuous snapshots of the Earth’s surface over long periods. River extraction from remote sensing images is useful for the comprehensive study of dynamic changes of rivers over large areas. This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. The use of a training map from erosion morphology helps to extract the non-predictive river’s curves in the image. The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river’s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. The proposed algorithm was tested on the Kuala Terengganu district, Malaysia, an area that includes a river, a bridge, dam and a fair amount of vegetation. The results were compared with two standard methods based on visual perception and on peak signal-to-noise ratio, respectively. The novelty of this approach is the definition of the contextual information filtering technique, which provides an accurate extraction of river segmentation from satellite images

Water-body segmentation in satellite imagery applying modified Kernel K-means

The main purpose of k-Means clustering is partitioning patterns into various homogeneous clusters by minimizing cluster errors, but the modified solution of k-Means can be recovered with the guidance of Principal Component Analysis (PCA). In this paper, the linear Kernel PCA guides k-Means procedure using filter to modify images in situations where some parts are missing by k-Means classification. The proposed method consists of three steps: 1) transformation of the color space and using PCA to solve the eigenvalue problem pertaining to the covariance matrices of satellite image; 2) feature extraction from selected eigenvectors and are rearranged by applying the training map to extract the useful information as a set of new orthogonal variables called principal components; and 3) classification of the images based on the extracted features using k-Means clustering. The quantitative results obtained using the proposed method were compared with k-Means and k-Means PCA techniques in terms of accuracy in extraction. The contribution of this approach is the modification of PCA selection to achieve more accurate extraction of the water-body segmentation in satellite images

Cloud-assisted gamification for education and learning – Recent advances and challenges

Gamification has gained considerable interest in education circles due to its capability of enhancing the learning process among students. In the future, it is expected that gamification will overtake the traditional way of learning resulting in issues such as scalability, upgradation of learning modules. To address these issues, merging gamification with cloud computing seems a viable solution. However, the employability of gamification through cloud computing is still in its infant stage. Hence, this article investigates the applicability of gamification through cloud computing and presents a comprehensive survey of state-of-the-art gamification in education and learning. We also identify the subject areas that can be gamified and taught using the cloud service. The critical elements and minimum requirements necessary to gamify education are also identified. Moreover, a specific cloud-assisted gamification architecture is proposed and discussed together with its possible applications. The article is concluded with the research challenges and suggestions for future work