The Soft Happy Colouring (SHC) problem, a mathematical framework for identifying homophilic network structures, seeks to maximise the number of -happy vertices, i.e., vertices with at least a proportion of neighbours that share the same colour. Because this NP-hard problem makes finding exact solutions intractable for large networks, probabilistic metaheuristics such as the Cross-Entropy (CE) method are suitable candidates. However, pure CE frequently suffers from stagnation of the probability distributions and non-convergence in high-dimensional spaces. To address this, we introduce CE+LS , synergising CE’s adaptive learning with a fast, structure-aware local search ( LS ). By restricting the search exclusively to local optima, CE+LS learns from high-quality structural characteristics rather than raw random samples. We mathematically and empirically demonstrate that this search space reduction resolves CE’s stagnation, yielding a convergent algorithm. Evaluating CE+LS across 28,000 Stochastic Block Model graphs, validated by non-parametric statistical testing, demonstrates that it consistently outperforms existing heuristic and memetic algorithms. Furthermore, benchmarking against the commercial exact solver, CPLEX, on real-world networks confirms that CE+LS identifies near-optimal configurations in a fraction of the required computational time for CPLEX. Crucially, CE+LS remains highly efficient even in the tight constraint regime, where comparative algorithms usually fail.

Abstract Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment Classification

Disseminating prior knowledge about a pattern recognition task in Deep Neural Networks (DNNs) is desirable, to enable them to learn some complex patterns or representations, that are otherwise difficult to learn via usual data-driven training. Several methods have been proposed for that purpose, but creating an end-to-end trainable DNN model, while keeping it informed with prior knowledge, remains a challenging task. In this paper, we propose a method to disseminate prior knowledge in DNN models. Specifically, we created a novel MAsk-based pRior Knowledge diSsemination mechanism (MARKS-mech), that transfers logical prior knowledge in DNN models via input data transformation. We utilize a recently constructed Twitter-based dataset to perform our experiments, which is specifically designed to test the logical prior knowledge dissemination ability of methods like ours. We find that our method provides superior knowledge dissemination performance compared to the baselines.

This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network with n disjoint output layers that do not constraint each other, (iii) may output predictions that are often inconsistent with the taxonomy in place, and (iv) have often a fixed value of n. Specifically, we propose a model agnostic output layer that embeds the taxonomy and that can be combined with any model. Our proposed output layer implements a top-down divide-and-conquer strategy through a masking mechanism to enforce that predictions comply with the embedded hierarchy structure. Focusing on image classification, we evaluate the performance of our proposed output layer on three different datasets, each with a three-level hierarchical structure. Experiments on these datasets show that our proposed mask-based output layer allows to improve several multi-level hierarchical classification models using various performance metrics.

This paper presents Marine-tree, a large-scale hierarchical annotated dataset for marine organism classification. Marine-tree contains more than 160k annotated images divided into 60 classes organised in a hierarchy-tree structure using an adapted CATAMI (Collaborative and Automated Tools for the Analysis of Marine Imagery and video) classification scheme. Images were meticulously collected by scuba divers using the RLS (Reef Life Survey) methodology and later annotated by experts in the field. We also propose a hierarchical loss function that can be applied to any multi-level hierarchical classification model, which takes into account the parent-child relationship between predictions and uses it to penalize inconsistent predictions. Experimental results demonstrate thatMarine-tree and the proposed hierarchical loss function are a good contribution for both research in underwater imagery and hierarchical classification.

We apply methods from randomized numerical linear algebra (RandNLA) to develop improved algorithms for the analysis of large-scale time series data. We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. We show that the accuracy of approximations lies within $(1+\mathcal{O}({\varepsilon}))$ of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Our proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach.

Balancing and sequencing of assembly lines is the process of partitioning the assembly work in terms of operations, and to assign and schedule them to workstations in an optimal way. In particular, in response to highly competitive market conditions, manufacturers face the problem of producing several models of a base product on the same assembly line, which leads to a mixed-model assembly line balancing problem. This problem is proven to be NP-hard and is computationally challenging. In addition to the usual problem constraints (e.g. precedences between operations and satisfying cycle times), we consider setup times between operations, which further complicates the problem. In this work, we present a novel ant colony optimisation approach, which is based on learning permutations of the operations. The permutations are then mapped to an assignment of operations to workstations in a greedy fashion. The numerical experiments demonstrate improvements both in the quality of solutions and significant improvements in computational time in comparison to the exact state of the art solution methods currently available in the literature.

Automatic classification algorithms are an important component of expert decision support systems that are used in a number of medical applications including diagnostic radiology and disease detection. This study proposes a deep learning-based framework for medical image classification using wavelet features. Convolutional neural networks are incorporated to discover informative latent patterns and features from a set of X-ray images pertaining to human body parts. The features are then passed to a classifier for labelling the respective X-ray images. The experimental results show that the low-pass filter wavelet-based convolutional model outperforms the original convolutional network and some models for classifying X-ray images. The performance of the proposed method implies that it can be implemented effectively in practice for disease detection using radiological images.

Deep neural networks have proven themselves as a strong approach in image classification and object detection with high accuracy. However, they are computationally demanding and the trained networks contain millions of active parameters and connections. Two recent trends of having deeper and dense architectures and the deployment of trained networks on resource-constrained devices such as smart phones and portable tablets bring new challenges. Instead of deploying an ensemble of smaller networks, we propose a pruning methodology on a trained network so that a smaller version of a fully trained network has the same and even better accuracy in comparison to the original one. We achieve two objectives with the pruning scheme. First, we have a smaller network with a better accuracy level, and we make the trained model avoids overfitting. Accordingly, an evolutionary based framework including three steps is defined to perform further tuning on trained deep network using dropping nodes and connections. This study shows that implementing genetic algorithm, after preprocessing and training stages, not only results in partially connected networks, but also increases performance and reduces overfitting specially when the depth and width of fully connected networks are investigated in small datasets.