Global warming is one of the main challenges of recent times. The glaciers are melting faster than expected which has resulted in global mean sea level rise and increased the risk of floods. The development of modern remote sensing technology has made it possible to obtain images more frequently than ever before. On the other side, the availability of high-performance computing hardware and processing techniques have made it possible to provide a cost-effective solution to monitor the temporal changes of glaciers at a large scale. In this study, supervised machine learning methods are investigated for automatic classification of glacier covers from multi-temporal Sentinel-2 imagery using texture, topographic, and spectral data. Three most commonly used supervised machine learning techniques were investigated: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The proposed method was employed on the data obtained from Passu watershed in Hunza Basin located along the Hunza river in Pakistan. Three main classes were considered: glaciers, debris-covered glaciers and non-glaciated areas. The data was split into training (70%) and testing datasets (30%). Finally, an area-based accuracy assessment was performed by comparing the results obtained for each classifier with the reference data. Experiments showed that the results produced for all classifiers were highly accurate and visually more consistent with the depiction of glacier cover types. For all experiments, random forest performed the best (Kappa= 0.95, f-measure =95.06%) on all three classes compared to ANN (Kappa= 0.92, f-measure =92.05) and SVM (Kappa= 0.89, f-measure =91.86% on average). The high classification accuracy obtained to distinguished debris-covered glaciers using our approach will be useful to determine the actual available water resources which can be further helpful for hazard and water resource management.

The emergence in computing and the latest hardware technologies realized the use of natural interaction with computers. Gesture-based interaction is one of the prominent fields of natural interactions. The recognition and application of hand gestures in virtual environments (VEs) need extensive calculations due to the complexities involved, which directly affect the performance and realism of interaction. In this paper, we propose a new interaction technique that uses single fingertip-based gestures for interaction with VEs. The objective of the study is to minimize the computational cost, increase performance, and improve usability. The interaction involves navigation, selection, translation, and release of objects. For this purpose, we propose a low-cost camera-based system that uses a colored fingertip for the fastest and accurate recognition of gestures. We also implemented the proposed interaction technique using the Leap Motion controller. We present a comparative analysis of the proposed system with the Leap Motion controller for gesture recognition and operation. A VE was developed for experimental purposes. Moreover, we conducted a comprehensive analysis of two different recognition setups including video camera and the Leap Motion sensor. The key parameters for analysis were task accuracy, interaction volume, update rate, and spatial distortion of accuracy. We used the Standard Usability Scale (SUS) for system usability analysis. The experiments revealed that camera implementation was found with good performance, less spatial distortion of accuracy, and large interaction volume as compared to the Leap Motion sensor. We also found the proposed interaction technique highly usable in terms of user satisfaction, user-friendliness, learning, and consistency.

Educational institutions demand cost-effective and simple-to-use augmented reality systems. ARToolKit, an open-source computer tracking library for the creation of augmented reality applications that overlay virtual imagery on the real world, is such a system. It uses a simple camera and black-and-white markers printed on paper. However, due to inter-marker confusion, if the marker distinctions are not ensured, the markers are often miss-recognized. This paper presents an ARToolKit-based Interactive Writing Board (IWB) with a simple mechanism for designing confusion-free marker libraries. The board is used for teaching single characters of Arabic/Urdu to primary level students. It uses a simple ARToolKit marker for the recognition of each character. After marker recognition, the IWB displays the corresponding image, helping students with character understanding and pronunciation. Experimental results reveal that the system improves students’ motivation and learning skills.

The study aims to explore the access of mobile phone, emerging technologies, and use of the mobile phone by the youth of Gilgit-Baltistan (GB), Pakistan, for learning and safety and security purposes. Structured questionnaires were used to collect the data. The descriptive statistics was employed to test the research model. Among the 300 distributed sample size, 272 participants responded back including 133 male and 139 female students of Karakoram International University (KIU) from eight districts of GB as research participants for data collection. Only 1 male and 6 females responded that they do not have their own mobile phone, comprising of 90.6 response rate. The results show that 97 of students of rural mountainous areas own a mobile phone. The study contributes valuable findings about the access and positive use of mobile phones for learning and safety and security purposes. The result also shows that the youth of GB have enough skills of mobile phone technologies to cope with the future challenges of China Pakistan Economic Corridor (CPEC) by taking advantages of China Pakistan Information Corridor (CPIC is a fiber optics cable laid down from the China border to Islamabad Pakistan for the purpose of providing the fast internet facility including 5G). This is the baseline survey and future study will be based on this survey.

This research aims to investigate the impact of mobile phones in the lives of youths of mountainous rural areas of Gilgit-Baltistan (GB). A total of 272 (133 male and 139 female) respondents of ages between 16 and 25 years participated in this study. To analyze the demographic data such as age, gender, district, the descriptive statistics (mean, SD and percentage) and inferential statistics such as independent sample t-test were used. The regression analysis was used to analyze the relationship between independent and dependent variables such as mobile phone features (M = 3.66, SD = 1.15); a mobile phone as a tool for socio-economic impact (M = 3.80, SD = 1.20); as a fashion symbol (M = 1.29, SD = 0.11) and a tool for safety (M = 3.91, SD = 1.06). The findings show that 97% (M = 1.026 SD = 0.159) of youths from GB own a mobile phone (47% male and 48% female). The findings also verify that a mobile phone is beneficial to its users in the fields of economic, education, safety, and security. However, using a mobile phone as status symbol could have a negative impact on the lives of youths. This study recommends that the government should develop effective and efficient policy for mobile phone usage and users should also be aware of the blessings and risks associated with using a mobile phone in their lives.

Schema merging is a process of integrating multiple data sources into a GCS (Global Conceptual Schema). It is pivotal to various application domains, like data ware housing and multi-databases. Schema merging requires the identification of corresponding elements, which is done through schema matching process. In this process, corresponding elements across multiple data sources are identified after the comparison of these data sources with each other. In this way, for a given set of data sources and the correspondence between them, different possibilities for creating GCS can be achieved. In applications like multi-databases and data warehousing, new data sources keep joining in and GCS relations are usually expanded horizontally or vertically. Schema merging approaches usually expand GCS relations horizontally or vertically as new data sources join in. As a result of such expansions, an unbalanced GCS is created which either produces too much NULL values in response to global queries or a result of too many Joins causes poor query processing. In this paper, a novel approach, TuSMe (Tuned Schema Merging) techniqueis introduced to overcome the above mentioned issue via developing a balanced GCS, which will be able to control both vertical and horizontal expansion of GCS relations. The approach employs a weighting mechanism in which the weights are assigned to individual attributes of GCS. These weights reflect the connectedness of GCS attributes in accordance with the attributes of the principle data sources. Moreover, the overall strength of the GCS could be scrutinized by combining these weights. A prototype implementation of TuSMe shows significant improvement against other contemporary state-of-the-art approaches.