Classroom Simulation for Trainee Teachers Using 3D Virtual Environments and Simulated Smartbot Student Behaviours

his thesis consists of an analysis of a classroom simulation using a Second Life (SL) experiment that aims to investigate the teaching impact on smartbots (virtual students) from trainee teacher avatars with respect to interaction, simulated behaviour, and observed teaching roles. The classroom-based SL experiments’ motivation is to enable the trainee teacher to acquire the necessary skills and experience to manage a real classroom environment through simulations of a real classroom. This type of training, which is almost a replica of the real-world experience, gives the trainee teacher enough confidence to become an expert teacher. In this classroom simulation, six trainee teachers evaluated the SL teaching experience by survey using qualitative and quantitative methods that measured interaction, simulated behaviour, and safety. Additionally, six observers evaluated trainee teachers’ performance according to a set of teaching roles and roleplay approaches. The experiment scenario was set up between smartbots, trainee teacher avatars, and observer avatars in the virtual classroom, where smartbots are intelligent agents managing SL bots, and where groups are similar to one another but are under programming control.Saudi Embassy in Londo

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain

Classifying Text-Based Emotions Using Logistic Regression

Emotion detection textual content is getting popular among individuals and business companies to analyze user emotional reaction on the products they use. In this work, emotion detection from textual content is performed by using supervised learning-based Logistic Regression classifier. ISEAR dataset is used to taring the classifier, while testing dataset is used to evaluate the prediction capability of the classifier for emotion classification. The prior works used rule-based techniques, supported by lexical resources. However, limited coverage of emotional clues, was the major issue, which resulted in poor performance of system. The proposed work overcomes this limitation by proposing supervised learning technique using Logistic Regression classifier. The results obtained are encouraging and show that the proposed system performed better than the similar methods

A Multifaceted Deep Generative Adversarial Networks Model for Mobile Malware Detection

Malware’s structural transformation to withstand the detection frameworks encourages hackers to steal the public’s confidential content. Researchers are developing a protective shield against the intrusion of malicious malware in mobile devices. The deep learning-based android malware detection frameworks have ensured public safety; however, their dependency on diverse training samples has constrained their utilization. The handcrafted malware detection mechanisms have achieved remarkable performance, but their computational overheads are a major hurdle in their utilization. In this work, Multifaceted Deep Generative Adversarial Networks Model (MDGAN) has been developed to detect malware in mobile devices. The hybrid GoogleNet and LSTM features of the grayscale and API sequence have been processed in a pixel-by-pixel pattern through conditional GAN for the robust representation of APK files. The generator produces syntactic malicious features for differentiation in the discriminator network. Experimental validation on the combined AndroZoo and Drebin database has shown 96.2% classification accuracy and a 94.7% F-score, which remain superior to the recently reported frameworks

Modeling and Performance Evaluation of Multi-Class Queuing System with QoS and Priority Constraints

Many service providers often categorize their users into multi-classes, depending on their service requirements. Each class has strict quality of service (QoS) demands (e.g., minimum required service rate or transfer time) that must be ensured throughout its service. In some cases, priorities are also assigned in a multi-class user’s environment to ensure that the important class user shall be serviced first. In this paper, we have developed a novel Markov chain based analytical model to investigate and evaluate a multi-class queuing system with a strict QoS requirement and priority constraints. Experimental analysis is conducted for two users classes, i.e., class-1 (may be free/student users) and class-2 (may be paid/research users). Each class requests have strict QoS requirements in terms of the minimum required rate (MRR) that must be ensured throughout its lifetime once the request is admitted into the system. Secondly, class-2 requests have preemption priority over class-1, i.e., if there is no room for newly arriving class-2 requests, then one or more active flows of class-1 can be ejected in order to accommodate high-class requests. Model results are validated through simulation results and performance measures of our interest include blocking probability (BP) of individual classes and the overall system, effect of higher-class jobs on lower-class jobs, and link capacity utilization. The proposed model can be instrumental in developing advanced connection admission control (CAC), efficient resource dimensioning, and capacity planning of the queuing system

Evaluating critical success factors in implementing E-learning system using multicriteria decision-making

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management