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Blockchain technology enables users to control and record their cryptocurrency transactions through the use of digital wallets. As the use of blockchain technology and cryptocurrency wallets continues to grow in popularity, the potential for attacks on these wallets increases, as attackers seek to gain access to the large sums of cryptocurrency they contain. To mitigate these risks, it is important to conduct thorough security evaluations of wallets and implement strong protective measures. In recent years, there have been several incidents involving significant losses of cryptocurrency in crypto-wallets, and in this research, a comprehensive evaluation of seed phrase and password attack methods found in the published literature was conducted, and the topic was advanced by addressing the question of whether seed phrases are hackable. The research aims to use the elliptic-curve cryptography (ECC) encryption algorithm for storing the seed phrase online by encrypting the seed phrase and using the splitting technique to store the crypto wallet seed phrase. It was concluded that it is only possible to hack a seed phrase if a significant portion of it is already known, but even this would require a significant amount of time and computational power.

In the modern landscape of information and communication technologies, the current healthcare industry confronts significant challenges. These include a shortage of experienced medical professionals, disparities in access to healthcare services that persist across different regions around the globe, and an increased need for detailed, real-time monitoring of patients in both urban and remote regions. This article delves into the potential of the Internet of Audio Things for Healthcare (IoAuT4H), which lies at the intersection of Internet of Audio Things and the Internet of Medical Things, as a solution to these pressing issues. By seamlessly merging cutting-edge audio technology, networking, and the advanced deep learning techniques, the IoAuT4H emerges as a promising solution. It has the potential to reshape hospital and clinical infrastructure, streamline early medical interventions, and facilitate rapid emergency responses. Additionally, this study underscores the pivotal role of the IoAuT4H in strengthening overall health practices, refining care methods for the elderly, and rejuvenating paediatric health approaches. While the benefits of the IoAuT4H are numerous, this article also critically examines the challenges in its widespread adoption. These include ethical considerations, ensuring the accuracy of audio data, and integrating it effectively with existing healthcare systems. In conclusion, this article seeks to provide a comprehensive understanding of the IoAuT4H, positioning it as a bridge between current healthcare challenges and technological advancements.

The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and context-aware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.

Despite the recent progress in emotion recognition, state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this article we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language multimodal emotion recognition. Our model utilises pre-trained models for multimodal feature extraction and is equipped with dual attention mechanisms including graph attention and co-attention to capture complex dependencies across different modalities and languages to achieve improved cross-language multimodal emotion recognition. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. This novel construct preserves modality-specific emotional information while enhancing cross-modality and cross-language feature generalisation, resulting in improved performance with minimal target language data. We assess our model's performance on four publicly available emotion recognition datasets and establish its superior effectiveness compared to recent approaches and baseline models.

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.