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Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.

Dynamic traffic in a software-defined network (SDN) causes explosive data to flow from one system to another. The explosive data affects the functionality of system parameters, network-level configuration, routing parameters, network characteristics, and system load factors. Adapting to the traffic flow is a key research area in SDN in today’s big data world. Load balance vehicular sensor accessibility reduces delays, lowers energy consumption, and decreases the execution time. This paper combines the entropy-based active learning model to identify intrusion patterns efficiently, which is a packet-level intrusion detection model. The developed afterload balancing model can track the attack on the network. We then proposed a load balancing algorithm that optimizes the vehicular sensor usability by using sensor computing capability and source needs. We make use of a convergence-based mechanism to achieve high resource utilization. We then perform experiments on the state-of-the-art intrusion detection dataset. Our experimental results show that the load balancing mechanism can achieve 2x in performance improvements compared to traditional approaches. Thus, we can see that the designed model can help improve the decision boundary by increasing the training instance through pooling strategy and entropy uncertainty measure.

Artificial Intelligence (AI) is increasingly becoming an important component of the education sector, both in primary education and in higher education. The rapid advancement of computing technologies and big data processing techniques has made it possible to implement AI in education. AI and machine learning techniques are now being used to deliver chatbots like ChatGPT; intelligent tutoring systems; automatic grading systems; and other advanced learning systems like AI Adaptive Learning Systems, in the education sector. These advanced learning systems, AI enabled Adaptive Learning Systems (AI-ALS), is the focus of my doctoral dissertation. AI-ALS are the most advanced generation of adaptive learning systems that use more sophisticated data analytics (learning analytics) and AI to provide just-in-time insights and embed ongoing data collection to improve the quality of adaptive learning. Adaptive learning is the personalization of learning for students in a learning system. Thus, AI-ALS are learning platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks. Adaptive learning systems, which personalize the learning experience to the needs and abilities of students, are a promising application of artificial intelligence in education. A growing body of research suggests that the use of AI-ALS in educational contexts has the potential to enhance teaching and learning skills as well as human performance. However, the effectiveness of these systems in real-world settings is not yet clear. While the potential and importance of such systems are well documented, the actual implementation status of AI-ALS and other AI-based learning systems in real-life teaching and learning settings is low and not fully understood. Also, the research on AI-ALS lacks insights and codification of its design and development knowledge. In addition, misuse of AI due to algorithm biases and lack of governance could also exacerbate inequalities in our educational institutions.

During crisis situations, emergency operators require fast information access to achieve situation awareness and make the best possible decisions. Augmented reality could be used to visualize the wealth of user-generated content available on social media and enable context-adaptive functions for emergency operators. Although emergency operators agree that social media analytics will be important for their future work, it poses a challenge to filter and visualize large amounts of social media data. We conducted a goal-directed task analysis to identify the situation awareness requirements of emergency operators. By collecting tweets during two storms in Germany we evaluated the usefulness of Twitter data for achieving situation awareness and conducted interviews with emergency operators to derive filter strategies for social media data. We synthesized the results by discussing how the unique interface of augmented reality can be used to integrate social media data into emergency control rooms for situation awareness support.

This paper investigates the attitude tracking control problem for uncertain nonlinear rigid body systems, where both inputs and states are quantized. It is common in networked control systems that sensor and control signals are quantized before they are transmitted via a communication network. An adaptive backstepping control algorithm is developed for a class of uncertain multiple-input multiple-output (MIMO) systems where a class of sector bounded quantizers is considered. It is shown that all the closed-loop signals are ensured uniformly bounded and tracking is achieved. Further, the tracking errors are shown to converge towards a compact set containing the origin and the set can be made small by the choice of the quantization parameters and the control parameters. For illustration of the proposed control scheme, experiments were conducted on a 2 degrees-of-freedom (DOF) helicopter system.

The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.