Pádraig Cunningham

Review papers have traditionally enjoyed a high status in academic publishing. This is due to the role that reviews and surveys can play in summarizing and synthesizing a field of research. From an author’s perspective, reviews have the added advantage that they pick up significantly more citations than primary research papers presenting original research. There has been a dramatic increase in the publication of review papers in recent years, both in raw numbers and as a proportion of overall publication output. In this paper we demonstrate this increase across a wide range of fields of study. We demonstrate that there is indeed a citation dividend associated with review papers although this dividend is declining. We also show that, since the arrival of Generative AI (GenAI) tools in 2022 there is evidence of wide use of GenAI in review paper writing. The use of GenAI tools seems to be more prevalent in review papers than in primary research papers. We suggest that the potential for GenAI to do a lot of the work in preparing review papers will have a significant impact on their status.

Review papers have traditionally enjoyed a high status in academic publishing because of the important role they can play in summarising and synthesising a field of research. They can also attract significantly more citations than primary research papers presenting original research, making them attractive to authors. There has been a dramatic increase in the publication of review papers in recent years, both in raw numbers and as a proportion of overall publication output. In this paper we demonstrate this increase across a wide range of fields of study. We quantify the citation dividend associated with review papers, but also demonstrate that it is declining and discuss the reasons for this decline. We further show that, since the arrival of GenAI tools in 2022 there is evidence of widespread use of GenAI in research paper writing, and we present evidence for a stronger AI signal among review papers compared to primary research papers. We suggest that the potential for GenAI to accelerate and even automate the production review papers will have a further significant impact on their status.

There has been some concern about the impact of predatory publishers on scientific research for some time. Recently, publishers previously viewed as ‘predatory’ have established their bona fides , at least to the extent that they are included in impact scores such as the field-weighted citation impact (FWCI). These are sometimes called grey publishers (MDPI, Frontiers, Hindawi). We show that the citation landscape for these grey publications is significantly different from the mainstream landscape and that affording grey publications the same status as mainstream journals may significantly distort metrics such as the FWCI.

In recent years, there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in the context of animal welfare in livestock science. In this paper, we report on research on animal activity recognition in support of welfare monitoring. The data comes from collar-mounted accelerometer sensors worn by Holstein and Jersey calves, the objective being to detect changes in behaviour indicating sickness or stress. A key requirement in detecting changes in behaviour is to be able to classify activities into classes, such as drinking, running or walking. In Machine Learning terms, this is a time-series classification task, and in recent years, the Rocket family of methods have emerged as the state-of-the-art in this area. We have over 27 hours of labelled time-series data from 30 calves for our analysis. Using this data as a baseline, we present Rocket's performance on a 6-class classification task. Then, we compare this against the performance of 11 Deep Learning (DL) methods that have been proposed as promising methods for time-series classification. Given the success of DL in related areas, it is reasonable to expect that these methods will perform well here as well. Surprisingly, despite taking care to ensure that the DL methods are configured correctly, none of them match Rocket's performance. A possible explanation for the impressive success of Rocket is that it has the data encoding benefits of DL models in a much simpler classification framework.

Estimating conditional probabilities from data samples is the building block of many machine learning algorithms. However, these estimates often do not reflect the true underlying distribution. With ML systems becoming more ubiquitous in high-risk decision making, it is critical to investigate how the predictions made by the algorithm can misrepresent the actual data distribution. To this end, we explore the notion of underestimation bias that captures the extent to which a learned model predictions deviate from the true distribution. Since fixing underestimation bias may come at the cost of accuracy, we propose a multi-objective optimization strategy to obtain a diverse set of models with members representing different accuracy/faithfulness tradeoffs. We empirically evaluate our framework on two synthetic and twelve real-world datasets. We show that our framework can address underestimation bias while still maintaining adequate overall generalization accuracy.

Algorithmic bias arises in machine learning when models that may have reasonable overall accuracy are biased in favor of ‘good’ outcomes for one side of a sensitive category, for example gender or race. The bias will manifest as an underestimation of good outcomes for the under-represented minority. In a sense, we should not be surprised that a model might be biased when it has not been ‘asked’ not to be; reasonable accuracy can be achieved by ignoring the under-represented minority. A common strategy to address this issue is to include fairness as a component in the learning objective. In this paper, we consider including fairness as an additional criterion in model training and propose a multi-objective optimization strategy using Pareto Simulated Annealing that optimizes for both accuracy and underestimation bias. Our experiments show that this strategy can identify families of models with members representing different accuracy/fairness tradeoffs. We demonstrate the effectiveness of this strategy on two synthetic and two real-world datasets.

Correlations in streams of multivariate time series data means that typically, only a small subset of the features are required for a given data mining task. In this paper, we propose a technique which we call Merit Score for Time-Series data (MSTS) that does feature subset selection based on the correlation patterns of single feature classifier outputs. We assign a Merit Score to the feature subsets which is used as the basis for selecting 'good' feature subsets. The proposed technique is evaluated on datasets from the UEA multivariate time series archive and is compared against a Wrapper approach for feature subset selection. MSTS is shown to be effective for feature subset selection and is in particular effective as a data reduction technique. MSTS is shown here to be computationally more efficient than the Wrapper strategy in selecting a suitable feature subset, being more than 100 times faster for some larger datasets while also maintaining a good classification accuracy.