Pattern recognition models, particularly neural networks, often focus on maximising classification accuracy. However, in practice, the types of errors made (misclassification between different classes) can have varying associated costs. Current methods overlook varying misclassification error types. Misclassification labels can either be available from expert knowledge or derived from semantics of textual descriptions of class labels. Exploiting such misclassification costs can have significant implications when deploying machine learning systems. Here, using five examples from image and tabular domains, we show how a deep neural architecture trained in a nested layer-wise fashion (cascade learning) in which early layers solve easier problems than later ones could exploit such hierarchical aspects of class labels. We employ a measure of performance called "severity" of errors and show how emphasis could be placed on classes that are deeper in the hierarchy, ignoring errors that arise between semantic neighbours. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-025-11613-8.

Representation transfer is a widely used technique in natural language processing. We propose methods of cleaning the dominant dataset of text simplification (TS) WikiLarge in multi-views to remove errors that impact model training and fine-tuning. The results show that our method can effectively refine the dataset. We propose to take the pre-trained text representations from a similar task (e.g., text summarization) to text simplification to conduct a continue-fine-tuning strategy to improve the performance of pre-trained models on TS. This approach will speed up the training and make the model convergence easier. Besides, we also propose a new decoding strategy for simple text generation. It is able to generate simpler and more comprehensible text with controllable lexical simplicity. The experimental results show that our method can achieve good performance on many evaluation metrics.

Information-enabled technology, such as digital contact tracing (DCT), is designed to assist decision-making. However, during the pandemic, DCT also contributes additional factors to consider for individuals and policymakers. In this article, we conceptualise the interactions between individuals and policymakers and propose a leader-followers mean-field Game model to analyse their decision-making processes by integrating key factors, such as individuals’ health state, self-control effort, privacy, and social activities, under the influence of interventions and subsidies from policymakers. The simulation demonstrates that moderate subsidies are sufficient to induce early control efforts and higher collective efforts even with privacy concerns and the negative influence of engaging activities; Equilibrium and system stability can be reached when healthy or the summation of healthy and mediocre healthy populations dominates; The model highlights the crucial elements for future data analysis and collection, such as DCT is vital to indicate population health states.

Neural Arithmetic Logic Modules have become a growing area of interest, though remain a niche field. These modules are neural networks which aim to achieve systematic generalisation in learning arithmetic and/or logic operations such as $\{+, -, \times, \div, \leq, \textrm{AND}\}$ while also being interpretable. This paper is the first in discussing the current state of progress of this field, explaining key works, starting with the Neural Arithmetic Logic Unit (NALU). Focusing on the shortcomings of the NALU, we provide an in-depth analysis to reason about design choices of recent modules. A cross-comparison between modules is made on experiment setups and findings, where we highlight inconsistencies in a fundamental experiment causing the inability to directly compare across papers. To alleviate the existing inconsistencies, we create a benchmark which compares all existing arithmetic NALMs. We finish by providing a novel discussion of existing applications for NALU and research directions requiring further exploration.

Cascade Learning (CL) [20] is a new adaptive approach to train deep neural networks. It is particularly suited to transfer learning, as learning is achieved in a layerwise fashion, enabling the transfer of selected layers to optimize the quality of transferred features. In the domain of Human Activity Recognition (HAR), where the consideration of resource consumption is critical, CL is of particular interest as it has demonstrated the ability to achieve significant reductions in computational and memory costs with negligible performance loss. In this paper, we evaluate the use of CL and compare it to end to end (E2E) learning in various transfer learning experiments, all applied to HAR. We consider transfer learning across objectives, for example opening the door features transferred to opening the dishwasher. We additionally consider transfer across sensor locations on the body, as well as across datasets. Over all of our experiments, we find that CL achieves state of the art performance for transfer learning in comparison to previously published work, improving F1 scores by over 15%. In comparison to E2E learning, CL performs similarly considering F1 scores, with the additional advantage of requiring fewer parameters. Finally, the overall results considering HAR classification performance and memory requirements demonstrate that CL is a good approach for transfer learning.

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers.

Smoking is linked to more than two million preventable deaths yearly. The widespread use of sensors embedded in everyday devices provides novel means for research on smoking. Smartphones and smartwatches can monitor smoking behavior, which could lead to the development of new methods for smoking reduction or cessation. However, smoking often co-occurs with other activities, such as drinking and eating, which makes the recognition of concurrent and overlapping smoking activities from wearable sensors challenging. In this paper, we proposed for the first time to use deep learning for the automatic detection of smoking activities. A Convolutional Neural Network (CNN) architecture was proposed, and this improved on previously reported performance results. We investigated the impact of various data preprocessing approaches that influence the CNN classification results with statistical features and raw sensor data. We also considered the individual performance of the smartwatch vs. the smartphone and the gyroscope vs. accelerometer sensors for smoking activity recognition. Considering a dataset of concurrent activities such as drinking, eating, smoking while sitting, standing, walking, and partaking in a group conversation, our CNN approach obtained an F1-score of 92-96% in person-independent evaluation.

Topological data analysis (TDA) is a branch of mathematics that analyzes the shape of high-dimensional data sets using geometry and algebra. TDA is used for data visualization which represents the relationship among elements using a network. Traditionally, TDA is quadratic in complexity and not commonly used for natural language processing. In this research, we visualize the relationship among words in a text block, words in a corpus and text blocks in a corpus. Text block represents a unit of a corpus such as, a web page in a web corpus, a chapter or section in a book corpus or a document in media corpus. This research proposes circular topology for representing words both for Local Context (LC) and Global Context (GC). Each text block is a set of sentences forming the LC. We found that feature words are extracted successfully from our LC analysis. The occurrence of extracted featured words in the corpus formed the GC. We evaluate this proposed simplified topological analysis on 3 different corpora: a single book corpus, a book corpus consisting of 7 books having 6020 narrations and a web corpus consisting of 990 web pages. The peripheral nature of the LC reduced the vocabulary size of the corpus significantly in O(nm) time where n is the number of text blocks and m is number of nouns in a sentence. GC analysis of featured words reflected useful properties of featured word movement which can be used to analyze topic evolution. GC analysis of text block points is aimed to find closely related text blocks in a radius. This reflected interesting results that need further supervised investigation. Research on topology driven natural language processing is in its infancy. This article contributes to this research field by introducing a method motivated by TDA to represent and visualize the peripheral nature of text block and corpus, by achieving success in dimensional reduction using local analysis and by simplifying the approach of complex topological analysis through localization.

Human interactions that are sensed ubiquitously by mobile phones can improve a significant number of public health problems, particularly helping to track the spread of disease. In this paper, we evaluate multiple avenues for the integration of high-resolution face to face Bluetooth-sensed interaction networks into standard epidemic models. Our goal is to evaluate the capacity of the different avenues of integration to track the spread of seasonal influenza on a real-world community of 72 individuals over a period of 17 weeks. The dataset considered contains real-time tracking of individual flu symptoms over the whole observation period, providing a concrete individualized source for evaluation. We obtain an error of less than 2 infected people on average for predicting the total number of individuals affected by the flu and precision of approximately 30% when predicting exactly which individual will become infected at a given time. To the best of our knowledge, this is the first study considering mobile phone Bluetooth-sensed interaction data for dynamic infectious disease simulation that is evaluated against real human influenza occurrence. Our remarkable results indicate that high-resolution mobile phone data can increase the predictive power of even the simplest of epidemic models.

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of doing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recommendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses semantic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collection, the ``Million Musical Tweets Dataset'' of listening events extracted from microblogs. Overall, we find that modeling listeners' location via Gaussian mixture models and computing similarities from these outperforms both cultural user models and collaborative filtering.

In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the unsupervised learning of topics describing people's most common locations jointly with the most common types of interactions at these locations. We further successfully predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.

We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including "going to work at 10am", "leaving work at night", or "staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as "being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines.