ABSTRACT Forecasting cryptocurrency prices remains challenging due to extreme volatility, regime‐dependent dynamics, and unstable cross‐asset correlations. Statistical methods such as ARIMA and GARCH assume stationarity and linear dependence structures, making them inadequate for capturing non‐linear temporal patterns in high volatile cryptocurrency data. Conventional machine learning methods often require hand‐crafted features and fail to capture the sequential temporal dependencies inherent in price series. Recurrent deep learning approaches such as recurrent neural networks (RNNs) and LSTMs address the issues but suffer from limited parallelization, vanishing gradients, and difficulty in learning multi‐scale temporal patterns. The advances in Transformer have demonstrated strong capability in capturing long‐range temporal dependencies through self‐attention while enabling parallel computation. However, canonical Transformer still struggle with noisy, volatile financial time series due to computational complexity and sensitivity to irrelevant temporal patterns. These limitations motivate the exploration of Transformer‐based architectures. Our study makes three key contributions. First, we propose MSPCIFormer, a novel Transformer‐based architecture that integrates multi‐scale patching with channel‐independent (CI) modelling to capture heterogeneous temporal dynamics while mitigating noise from time‐varying inter‐asset correlations. Second, we conduct comprehensive experiments comparing MSPCIFormer with state‐of‐the‐art Transformer models and strong time‐series forecasting baselines, evaluated using both statistical and economic metrics across multiple forecasting horizons. Third, we establish a unified evaluation framework incorporating both Hold‐out and Walk‐forward evaluation framework with economical metrics for regime‐robustness testing. Empirical results demonstrate that MSPCIFormer achieves the best or tied‐best predictive accuracy among all Transformer‐based baselines across three cryptocurrency assets, while maintaining competitive and stable performance across diverse market regimes.

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

Data breaches and other security incidents are an emerging challenge in the digital era. The General Data Protection Regulation (GDPR) requires conducting an impact assessment to understand the effects of the breach, and to then notify authorities and affected individuals in certain cases. Communication of this information typically takes place via conventional mediums such as emails and forms on the websites of authorities, and is a manual process. To assist in developing tools to support data breach investigations, and to enable automated systems for assisting with breach assessments and GDPR compliance, we present a machine-readable specification for the representation and documentation of information related to data breaches and their communications. The specification uses current requirements from the GDPR obligations and authoritative guidelines. To represent information, it extends the Data Privacy Vocabulary (DPV) by introducing new concepts required for data breach relevant information.

Cryptocurrencies have won a lot of attention as an investment tool in recent years. Specific research has been done on cryptocurrencies’ price prediction while the prices surge up. Classic models and recurrent neural networks are applied for the time series forecast. However, there remains limited research on how the Transformer works on forecasting cryptocurrencies price data. This paper investigated the forecasting capability of the Transformer model on Bitcoin (BTC) price data and Ethereum (ETH) price data which are time series with high fluctuation. Long short term memory model (LSTM) is employed for performance comparison. The result shows that LSTM performs better than Transformer both on BTC and ETH price prediction. Furthermore, in this paper, we also investigated if sentiment analysis can help improve the model’s performance in forecasting future prices. Twitter data and Valence Aware Dictionary and sEntiment Reasoner (VADER) is used for getting sentiment scores. The result shows that the sentiment analysis improves the Transformer model’s performance on BTC price but not ETH price. For the LSTM model, the sentiment analysis does not help with prediction results. Finally, this paper also shows that transfer learning can help on improving the Transformer’s prediction ability on ETH price data.

Due to the COVID19 pandemic, more higher-level education programmes have moved to online channels, raising issues in monitoring students’ learning progress. Thanks to advances in online learning systems, however, student data can be automatically collected and used for the investigation and prediction of the students’ learning performance. In this article, we present a novel approach to analyse students’ learning behaviour, as well as the relationship between these behaviours and learning assessment results, in the context of programming education. A bespoke method has been built based on a combination of Random Matrix Theory, a Community Detection algorithm and statistical hypothesis tests. The datasets contain fine-grained information about students’ learning behaviours in two programming courses over two academic years with about 400 first-year students in a Medium-sized Metropolitan University in Dublin. The proposed method is a noval approach to data preprocessing which can improve the analysis and prediction based on learning behavioural datasets. The proposed approach deals with the issues of noise and trend effect in the data and has shown its success in detecting groups of students who have similar learning behaviours and outcomes. The higher performing groups have been found to be more active in practical-related activities throughout the course. Conversely, we found that the lower performing groups engage more with lecture notes instead of doing programming tasks. The learning behaviours data can also be used to predict students’ outcomes (i.e. Pass or Fail the terminal exams) at the early stages of the study, using popular machine learning classification techniques.

Received total wideband power (RTWP) data is a measurement of the wanted and unwanted power levels received by a 3G radio base station (RBS) and is a concise indicator of uplink network performance. Using a statistical physics approach, we aim to detect periods of unusual activity between cells by assessing a sample of RTWP measurement data from a live network. Using wavelet correlation and cross-correlation techniques we analyse multivariate non-stationary time series for statistical relationships at different time scales. We analyse the seasonal component of the dataset as well as examining the autocorrelation and partial autocorrelation methods. We then explore the Hurst exponent of the dataset and inspect the intraday correlations for patterns of events. Next, we examine the eigenvalue spectrum using different sized sliding windows. Finally, we compare approaches for assessing multiscale relationships among several variables using the wavelet multiple correlation and wavelet zero-lag cross-correlation on non-stationary RTWP time series data.

Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract\nmeaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted\nhuge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed\nin this project involves using data mining techniques: mining text documents such as news articles and tweets\ntry to infer the relationship between information contained in such items and cryptocurrency price direction.\nThe Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model\nwhich comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success\nof the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings\nreported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations\nwhen compared to other existing models in the market.

Dementia is one of the most common diseases in the elderly people. Experience shows that Microsoft's SenseCam can be an effective memory-aid device, as it helps users to improve recollecting an experience by creating visual lifelogs. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge to deconstruct a sizeable collection of images into meaningful events for users. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to “distinct significant events” in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images.

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.

BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.