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Frequency domain manipulation of multiple copy-move forgery in digital image forensics
Get PDFCopy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-move forgery detection, conventional approaches are generally effective at identifying simple multiple copy-move forgeries. However, the conventional approaches and deep learning approaches often fall short in detecting multiple forgeries when transformations are applied to the copied regions. Motivated from these findings, a transform domain method for generating and analyzing multiple copy-move forgeries is proposed in this paper. This method utilizes the discrete wavelet transform (DWT) to decompose the original and patch image into approximate (low frequency) and detail coefficients (high frequency). The patch image approximate and details coefficients are inserted into the corresponding positions of the original image wavelet coefficients. The inverse DWT (IDWT) reconstructs the processed image planes after modification which simulates the multiple copy move forgery. In addition, this approach is tested by resizing the region of interest with varying patch sizes resulting in an interesting set of outcomes when evaluated against existing state-of-the-art techniques. This evaluation allows us to identify gaps in existing approaches and suggest improvements for creating more robust detection techniques for multiple copy-move forgeries
Mergers and Acquisitions and Brexit: A Natural Experiment
No full textWe study the impact of Brexit uncertainty on one of the most important forms of corporate investment: mergers and acquisitions (M&As). Brexit provides us with an ideal natural experiment to explore the real effects of economic uncertainty and understand the underlying transmission mechanism. We document a significant decline in the number of M&A deals for UK firms after Brexit compared to EU firms. This inhibiting effect is amplified by the channels of real options, foreign trade, and financial constraints. Overall, our results provide for deeper understanding of this unprecedented uncertainty in Brexit policy on local M&A activity. Policy makers are urged to respond
The efficacy, feasibility, and technical outcomes of a GPT-4o-based chatbot Amanda for relationship support: A randomized controlled trial
No full textThis randomized controlled trial evaluated the efficacy, feasibility, and technical outcomes of Amanda, a GPT-4-based chatbot, in delivering single-session relationship interventions. A total of 258 participants were randomly assigned to engage with either Amanda (n = 130) or a writing task (n = 128) focused on conflict reappraisal. Findings demonstrated significant improvements across 13 of 14 outcome variables—including relationship satisfaction, communication patterns, dyadic coping, problem-specific confidence, and individual well-being—over time in both conditions. Improvements emerged immediately after the intervention and were sustained or continued to improve at the two-week follow-up. However, there were no significant group differences for most outcomes, suggesting that both interventions were comparably effective. One significant group-by-time interaction emerged: participants in the chatbot condition reported lower levels of the partner-demand/self-withdraw communication pattern immediately post-intervention. The writing condition was also associated with lower overall distress about the issue. Feasibility outcomes indicated strong participant engagement with Amanda. Usability was rated highly (M = 4.19/5), as were therapeutic skills (M = 3.99/5) and working alliance (M = 4.75/6). Technical evaluation of interaction transcripts supported these findings, with high coder agreement on Amanda’s empathy, therapeutic questioning, and coherence. However, limitations were noted: Amanda occasionally produced repetitive or generic responses and did not consistently identify potential safety concerns. Overall, results suggest that Amanda provides a feasible and effective single-session relationship intervention, comparable in impact to an evidence-based writing task. This study highlights the potential for large language model-based chatbots to deliver scalable, accessible relationship support. Future research should assess Amanda’s use in multi-session interventions, explore performance in clinical populations, and enhance risk detection capabilities to ensure safe deployment in real-world settings
Edge-Driven Disability Detection and Outcome Measurement in IoMT Healthcare for Assistive Technology
No full textThe integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility
The demographic transition and stagnation in countries vulnerable to climate change
No full textClimate change, degradation of local essential resources and high population growth could create long-lasting poverty traps and economic stagnation in Sub-Sahara Africa. We develop a theoretical framework that sheds light on mechanisms of economic stagnation that is driven by environmental degradation. Under poor basic infrastructures, environmental conditions impact intra-household labor allocation due to their effects on local essential resources such as water and/or firewood. Climate change damages essential resources, resulting in women having to spend more time collecting them for their families. This, in turn, leads parents to invest less in education for their daughters, resulting in gender inequality in education and income, delaying declines in fertility and creating population momentum. A larger population exacerbates the problem by further degrading essential resources through expanded production, reinforcing stagnation into a low development phase. The interplay between essential resources, gender inequality, and population, under the persistent effect of climate change, may thus generate a slow demographic transition and stagnation. Empirical evidence from 44 Sub-Saharan African countries during the period 1960-2017 confirms our theoretical predictions, emphasizing the urgent need to address these issues to promote sustainable development
Protocol for the process evaluation of a cluster randomised controlled trial evaluating the effectiveness and cost-effectiveness of a school-based intervention to prevent anxiety and depression in Malaysia: the MyHeRo study
No full textTransforming Smart Healthcare Systems with AI-Driven Edge Computing for Distributed IoMT Networks
No full textThe Internet of Medical Things (IoMT) with edge computing provides opportunities for the rapid growth and development of a smart healthcare system (SHM). It consists of wearable sensors, physical objects, and electronic devices that collect health data, perform local processing, and later forward it to a cloud platform for further analysis. Most existing approaches focus on diagnosing health conditions and reporting them to medical experts for personalized treatment. However, they overlook the need to provide dynamic approaches to address the unpredictable nature of the healthcare system, which relies on public infrastructure that all connected devices can access. Furthermore, the rapid processing of health data on constrained devices often leads to uneven load distribution and affects the system’s responsiveness in critical circumstances. Our research study proposes a model based on AI-driven and edge computing technologies to provide a lightweight and innovative healthcare system. It enhances the learning capabilities of the system and efficiently detects network anomalies in a distributed IoMT network, without incurring additional overhead on a bounded system. The proposed model is verified and tested through simulations using synthetic data, and the obtained results prove its efficacy in terms of energy consumption by 53%, latency by 46%, packet loss rate by 52%, network throughput by 56%, and overhead by 48% than related solutions
A Model Driven Framework for Gamification of Learning Introductory Programming
No full textProgramming is widely recognized as a fundamental and practical skill applicable across diverse fields through various applications. However, novices often face challenges in learning programming, primarily due to the absence of a structured instructional framework and the complexity of underlying concepts. This obstacle can diminish learners' motivation to pursue further education. To address this, gamification is employed as a strategy to engage and inspire beginners in their educational journey. Consequently, the utilization of a gamified online programming education system is proposed to simplify the learning process. Nevertheless, designing and implementing educational courses that effectively integrate gaming elements requires expertise in the gaming field. In this study, a model-driven approach creates a gamification framework for teaching programming. The methodology develops a domain-specific modeling language for programming concepts and gamification, designs a graphical editor for course design, and implements a model-to-code transformation engine requiring minimal prior knowledge. Evaluation through usability testing, questionnaires, and the GQM approach shows enhanced usability, improved effectiveness, and high satisfaction compared to traditional methods. The framework offer a solution for simplifying gamified course development and supporting novice programmers<br/