We introduce SigNova, a new semi-supervised framework for detecting anomalies in streamed data. While our initial examples focus on detecting radio-frequency interference (RFI) in digitized signals within the field of radio astronomy, it is important to note that SigNova's applicability extends to any type of streamed data. The framework comprises three primary components. Firstly, we use the signature transform to extract a canonical collection of summary statistics from observational sequences. This allows us to represent variable-length visibility samples as finite-dimensional feature vectors. Secondly, each feature vector is assigned a novelty score, calculated as the Mahalanobis distance to its nearest neighbor in an RFI-free training set. By thresholding these scores we identify observation ranges that deviate from the expected behavior of RFI-free visibility samples without relying on stringent distributional assumptions. Thirdly, we integrate this anomaly detector with Pysegments, a segmentation algorithm, to localize consecutive observations contaminated with RFI, if any. This approach provides a compelling alternative to classical windowing techniques commonly used for RFI detection. Importantly, the complexity of our algorithm depends on the RFI pattern rather than on the size of the observation window. We demonstrate how SigNova improves the detection of various types of RFI (e.g., broadband and narrowband) in time-frequency visibility data. We validate our framework on the Murchison Widefield Array (MWA) telescope and simulated data and the Hydrogen Epoch of Reionization Array (HERA).

In human activity recognition (HAR), the assumption that training and testing data are independent and identically distributed (i.i.d.) often fails, particularly in cross-user scenarios where data distributions vary significantly. This discrepancy highlights the limitations of conventional domain adaptation methods in HAR, which typically overlook the inherent temporal relations in time-series data. To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i.i.d. assumption and leveraging temporal relations to align data distributions effectively across different users. This method combines the strengths of Variational Autoencoder (VAE) and Universal Sequence Mapping (USM) to capture and utilize common temporal patterns between users for improved activity recognition. Our results, evaluated on two public HAR datasets (OPPT and PAMAP2), demonstrate that CVAE-USM outperforms existing state-of-the-art methods, offering a more accurate and generalizable solution for cross-user activity recognition.

Most data-to-text datasets are for English, so the difficulties of modelling data-to-text for low-resource languages are largely unexplored. In this paper we tackle data-to-text for isiXhosa, which is low-resource and agglutinative. We introduce Triples-to-isiXhosa (T2X), a new dataset based on a subset of WebNLG, which presents a new linguistic context that shifts modelling demands to subword-driven techniques. We also develop an evaluation framework for T2X that measures how accurately generated text describes the data. This enables future users of T2X to go beyond surface-level metrics in evaluation. On the modelling side we explore two classes of methods - dedicated data-to-text models trained from scratch and pretrained language models (PLMs). We propose a new dedicated architecture aimed at agglutinative data-to-text, the Subword Segmental Pointer Generator (SSPG). It jointly learns to segment words and copy entities, and outperforms existing dedicated models for 2 agglutinative languages (isiXhosa and Finnish). We investigate pretrained solutions for T2X, which reveals that standard PLMs come up short. Fine-tuning machine translation models emerges as the best method overall. These findings underscore the distinct challenge presented by T2X: neither well-established data-to-text architectures nor customary pretrained methodologies prove optimal. We conclude with a qualitative analysis of generation errors and an ablation study.

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in safety-critical applications. In this work we consider real-world problems arising in aviation and other safety-critical areas, and investigate their requirements for a certified model. To this end, we investigate methodologies from the machine learning research community aimed towards verifying robustness and reliability of deep learning systems, and evaluate these methodologies with regard to their applicability to real-world problems. Then, we establish a new framework towards deep learning certification based on (i) inherently safe design, and (ii) run-time error detection. Using a concrete use case from aviation, we show how deep learning models can recover disentangled variables through the use of weakly-supervised representation learning. We argue that such a system design is inherently less prone to common model failures, and can be verified to encode underlying mechanisms governing the data. Then, we investigate four techniques related to the run-time safety of a model, namely (i) uncertainty quantification, (ii) out-of-distribution detection, (iii) feature collapse, and (iv) adversarial attacks. We evaluate each for their applicability and formulate a set of desiderata that a certified model should fulfill. Finally, we propose a novel model structure that exhibits all desired properties discussed in this work, and is able to make regression and uncertainty predictions, as well as detect out-of-distribution inputs, while requiring no regression labels to train. We conclude with a discussion of the current state and expected future progress of deep learning certification, and its industrial and social implications.

To thoroughly assess the mathematical reasoning abilities of Large Language Models (LLMs), we need to carefully curate evaluation datasets covering diverse mathematical concepts and mathematical problems at different difficulty levels. In pursuit of this objective, we propose FineMath in this paper, a fine-grained mathematical evaluation benchmark dataset for assessing Chinese LLMs. FineMath is created to cover the major key mathematical concepts taught in elementary school math, which are further divided into 17 categories of math word problems, enabling in-depth analysis of mathematical reasoning abilities of LLMs. All the 17 categories of math word problems are manually annotated with their difficulty levels according to the number of reasoning steps required to solve these problems. We conduct extensive experiments on a wide range of LLMs on FineMath and find that there is still considerable room for improvements in terms of mathematical reasoning capability of Chinese LLMs. We also carry out an in-depth analysis on the evaluation process and methods that have been overlooked previously. These two factors significantly influence the model results and our understanding of their mathematical reasoning capabilities. The dataset will be publicly available soon.

Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. However, the considered output candidates of the underlying search algorithm are under-explored and under-explained. We tackle this shortcoming by proposing a tree-in-the-loop approach, where a visual representation of the beam search tree is the central component for analyzing, explaining, and adapting the generated outputs. To support these tasks, we present generAItor, a visual analytics technique, augmenting the central beam search tree with various task-specific widgets, providing targeted visualizations and interaction possibilities. Our approach allows interactions on multiple levels and offers an iterative pipeline that encompasses generating, exploring, and comparing output candidates, as well as fine-tuning the model based on adapted data. Our case study shows that our tool generates new insights in gender bias analysis beyond state-of-the-art template-based methods. Additionally, we demonstrate the applicability of our approach in a qualitative user study. Finally, we quantitatively evaluate the adaptability of the model to few samples, as occurring in text-generation use cases.

Recent advancements in Natural Language Processing (NLP) with Large Language Models (LLMs) have made the sentiment analysis of financial news by machines a practical achievement and no longer just a dream. More precisely, Large Language Models (LLMs) have marked a major step forward in processing large contexts, exhibiting human-level performance on various professional and academic benchmarks, although they still have limitations such as reliability issues and limited context windows [OpenAI, 2023]. Their ability to process more context has shown particularly interesting applications in many business areas [George and George, 2023]. Hence exploring the potential to extract either weak or strong signals from financial news to enhance a risk-on risk-off investment strategy becomes highly pertinent. Indeed, extracting sentiment from financial news is not new [Tetlock, 2007, Schumaker and Chen, 2009], and finance has a longstanding tradition of exploiting textual data [Kearney and Liu, 2014].

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.

Current research on human activity recognition (HAR) mainly assumes that training and testing data are drawn from the same distribution to achieve a generalised model, which means all the data are considered to be independent and identically distributed $\displaystyle (i.i.d.) $. In many real-world applications, this assumption does not hold, and collected training and target testing datasets have non-uniform distribution, such as in the case of cross-user HAR. Domain adaptation is a promising approach for cross-user HAR tasks. Existing domain adaptation works based on the assumption that samples in each domain are $\displaystyle i.i.d. $ and do not consider the knowledge of temporal relation hidden in time series data for aligning data distribution. This strong assumption of $\displaystyle i.i.d. $ may not be suitable for time series-related domain adaptation methods because the samples formed by time series segmentation and feature extraction techniques are only coarse approximations to $\displaystyle i.i.d. $ assumption in each domain. In this paper, we propose the temporal relation optimal transport (TROT) method to utilise temporal relation and relax the $\displaystyle i.i.d. $ assumption for the samples in each domain for accurate and efficient knowledge transfer. We obtain the temporal relation representation and implement temporal relation alignment of activities via the Hidden Markov model (HMM) and optimal transport (OT) techniques. Besides, a new regularisation term that preserves temporal relation order information for an improved optimal transport mapping is proposed to enhance the domain adaptation performance. Comprehensive experiments are conducted on three public activity recognition datasets (i.e. OPPT, PAMAP2 and DSADS), demonstrating that TROT outperforms other state-of-the-art methods.

In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($\displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear gap in domain adaptation techniques is the neglect of the temporal relation embedded within time series data during the phase of aligning data distributions. Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method. This novel method uniquely recognises and integrates temporal relations during the domain adaptation process. By synergizing the capabilities of generative models with the Temporal Relation Attention mechanism, our method improves the classification performance in cross-user HAR. A comprehensive evaluation has been conducted on three public sensor-based HAR datasets targeting different scenarios and applications to demonstrate the efficacy of the proposed DGDATA method.