In the rapidly evolving landscape of medical record management, the traditional methods often grapple with issues related to data security, integrity, and accessibility. This paper introduces a groundbreaking approach to pediatric medical data management by leveraging the robust capabilities of blockchain, Non-Fungible Tokens (NFTs), InterPlanetary File System (IPFS), and distributed ledgers. Our proposed model meticulously addresses the limitations of the conventional systems by ensuring data immutability, transparency, and decentralized control. Starting with the creation of a unique Global ID for children, we outline a detailed 10-step approach to data storage, query, and update, emphasizing the pivotal roles of smart contracts and NFTs in guaranteeing data authenticity and uniqueness. The implementation section delves deeper into the intricacies of transaction creation, data query, and update mechanisms, underscoring the importance of secure interfaces, rigorous verification processes, and seamless synchronization with decentralized storage solutions. With the confluence of these advanced technologies, our approach promises a transformative shift in pediatric healthcare, simplifying processes for healthcare professionals and ensuring data security and privacy for patients.
Fast and reliable identification of cyber attacks in network systems of smart cities is currently a critical and demanding task. Machine learning algorithms have been used for intrusion detection, but the existing data sets intended for their training are often imbalanced, which can reduce the effectiveness of the proposed model. Oversampling and undersampling techniques can solve the problem but have limitations, such as the risk of overfitting and information loss. Furthermore, network data logs are noisy and inconsistent, making it challenging to capture essential patterns in the data accurately. To address these issues, this study proposes using Generative Adversarial Networks to generate synthetic network traffic data. The results offer new insight into developing more effective intrusion detection systems, especially in the context of smart cities' network infrastructure.
The field of Artificial Intelligence (AI) has a significant impact on the way computers and humans interact. The topic of (facial) emotion recognition has gained a lot of attention in recent years. Majority of research literature focuses on improvement of algorithms and Machine Learning (ML) models for single data sets. Despite the impressive results achieved, the impact of the (training) data quality with its potential biases and annotation discrepancies is often neglected. Therefore, this paper demonstrates an approach to detect and evaluate annotation label discrepancies between three separate (facial) emotion recognition databases by Transfer Testing with three ML architectures. The findings indicate Transfer Testing to be a new promising method to detect inconsistencies in data annotations of emotional states, implying label bias and/or ambiguity. Therefore, Transfer Testing is a method to verify the transferability of trained ML models. Such research is the foundation for developing more accurate AI-based emotion recognition systems, which are also robust in real-life scenarios.
This study proposes an integrated approach to image similarity measurement by extending traditional methods that concentrate on local features to incorporate global information. Global information, including background, colors, spatial representation, and object relations, can leverage the ability to distinguish similarity based on the overall context of an image using natural process techniques. We employ Video-LLaMA model to extract textual descriptions of images through question prompts, and apply cosine similarity metrics, BERTScore, to quantify image similarities. We conduct experiments on images of the same and different topics using various pre-trained language model configurations. To validate the coherence of the generated text descriptions with the actual theme of the image, we generate images using DALL-E 2 and evaluate them using human judgement. Key findings demonstrate the effectiveness of pre-trained language models in distinguishing between images depicting similar and different topics with a clear gap in similarity.
This article presents singularization, a new family of Moving Target Defense (MTD) strategy that we propose to strengthen the robustness of sensitive applets on SIMs without needing a full replacement of SIMs.
