We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evaluate modularity-maximizing using fast greedy and leading eigenvector approximations, infomap, walktrap and label propagation algorithms. For this purpose, we propose to construct a label co-occurrence graph (both weighted and unweighted versions) based on training data and perform community detection to partition the label set. Then, each partition constitutes a label space for separate multi-label classification sub-problems. As a result, we obtain an ensemble of multi-label classifiers that jointly covers the whole label space. Based on the binary relevance and label powerset classification methods, we compare community detection methods to label space divisions against random baselines on 12 benchmark datasets over five evaluation measures. We discover that data-driven approaches are more efficient and more likely to outperform RAkELd than binary relevance or label powerset is, in every evaluated measure. For all measures, apart from Hamming loss, data-driven approaches are significantly better than RAkELd ( $\alpha =0.05$ ), and at least one data-driven approach is more likely to outperform RAkELd than a priori methods in the case of RAkELd’s best performance. This is the largest RAkELd evaluation published to date with 250 samplings per value for 10 values of RAkELd parameter k on 12 datasets published to date.

On the basis of a semi-realistic SIR microsimulation for Germany and Poland, we show that the R ₀ parameter interval for which the COVID-19 epidemic stays overcritical but below the capacity limit of the health care system to reach herd immunity is so narrow that a successful implementation of this strategy is likely to fail, which is in contrast to results obtained from classical differential equation models. Our microsimulation is based on official census data and involves household composition and age distribution as the main population structure variables. Outside household contacts are characterised by an out-reproduction number R ^* which is the only free parameter of the model. For a subcritical domain we compute the time till extinction and prevalence as a function of the initial number of infected individuals and R ^* . For the Polish city of Wrocław we also discuss the combined impact of testing coverage and contact reduction. For both countries we estimate R ^* for disease progression until 20th of March 2020.

We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words) in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners) applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature.

Software development support tools have been studied for a long time, with recent approaches using Large Language Models (LLMs) for code generation. These models can generate Python code for data science and machine learning applications. LLMs are helpful for software engineers because they increase productivity in daily work. An LLM can also serve as a "mentor" for inexperienced software developers, and be a viable learning support. High-quality code generation with LLMs can also be beneficial in geospatial data science. However, this domain poses different challenges, and code generation LLMs are typically not evaluated on geospatial tasks. Here, we show how we constructed an evaluation benchmark for code generation models, based on a selection of geospatial tasks. We categorised geospatial tasks based on their complexity and required tools. Then, we created a dataset with tasks that test model capabilities in spatial reasoning, spatial data processing, and geospatial tools usage. The dataset consists of specific coding problems that were manually created for high quality. For every problem, we proposed a set of test scenarios that make it possible to automatically check the generated code for correctness. In addition, we tested a selection of existing code generation LLMs for code generation in the geospatial domain. We share our dataset and reproducible evaluation code on a public GitHub repository1, arguing that this can serve as an evaluation benchmark for new LLMs in the future. Our dataset will hopefully contribute to the development new models capable of solving geospatial coding tasks with high accuracy. These models will enable the creation of coding assistants tailored for geospatial applications.

In recent years, many studies on urban mobility based on large data sets have been published: most of them are based on crowdsourced GPS data or smart-card data. We present, what is to the best of our knowledge, the first exploration of public transport delay data harvested from a large-scale, official public transport positioning system, provided by the Wrocław municipality. We introduce the methodology to analyze the distribution of delays in public transport, enabling the improvement of timetables by making them more realistic, and thus improve passenger comfort. We evaluate the method considering the characteristics of delays between stops in relation to the direction, time, and delay variance of 1648 stop pairs from 16-mln delay reports. We construct a normalized feature matrix of likelihood of a given delay change happening at a given hour on the edge between two stops. We then calculate the distances between such matrices using the earth mover's distance and cluster them using hierarchical agglomerative clustering with Vor Hees's linkage method. As a result, we obtained six profiles of delay changes in Wrocław: edges nearly not impacting the delay at all, these not impacting the delay significantly, likely to cause strong increase of delay, these causing increase of delay, edges likely to cause strong decrease of delay, and finally these likely to cause decrease of delay (i.e., when a public transport vehicle is speeding). We analyze the spatial and mode of transport properties of each cluster and provide insights into reasons of delay change patterns in each of the detected profiles. Such insights can be successfully utilized in traffic structure optimization and transport model split.

It has been shown in this paper that simplistic Bag of Words (BoW) lexicon methods for sentiment polarity assignment with ensemble classifiers are much faster than a supervised approach to sentiment classification while yielding similar accuracy. BoW methods also proved to be efficient and fast across all examined datasets. Moreover, a new approach to lexicon extraction that can be successfully used for sentiment polarity assignment is presented in the paper. It has been shown that accuracy obtained from such lexicons outperforms other lexicon based approaches.