dataesr/scientific-paragraphs-categorization huggingface dataset:
One way where GeoAI can help initiate participation is to use existing data to identify people's interests based on their location among other parameters and suggest they engage in a project of their interest. Thereby the advertisement for the project is targeting the interested community. This is similar to what is being done in Facebook News Feed, YouTube suggestions, etc. (Cotter et al. , 2017; Roth et al. , 2020) , but for integrating the geographical space. Furthermore, receiving communication about the contributed data is another important key to sustaining participation. One possibility to address this is to provide people with automatic machine-generated feedback which is centered on the users' location. One example is the use of trained models on species distribution to provide feedback on the probability of observing a certain species in a particular location  (Lotfian et al.
Challenges and opportunities Visual analytics (VA) frameworks leverage visual interfaces to support analytical reasoning and can bridge problem spaces where human decision-making and reasoning are superior to machines (e. g. , semantic interpretation, object, and pattern recognition). In addition, VA can maximize advantages where machines are superior to humans (e. g. , real-time advanced computations and infinite recall-on-demand) to support complex reasoning by humans. VA approaches are inherently those with humans-in-the-loop, a concept that frequently appears in the AI community in recent years (e. g. ,  Zanzotto, 2019; Wu et al. , 2022)  and refers to the strengths of human cognition as an equal counterpart in analytical processes. This bridge between computational and visual methods is often constructed as a dynamic, coordinated-view visual interface (a 'dashboard') connected to a backend where a database and computational infrastructure supporting traditional statistics as well as machine learning/AI can be utilized.
, 2020a). Considering the large volume and complexity of geographical data, the design of effective geovisualization and visual analytics approaches that integrate human strengths with computational tools (i. e. , artificial intelligence that is critical to big data analytics) is also a core research aim within GIScience. In recent years, geovisual analytics (GeoVA) has emerged where computational analysis is directed by humans interacting with geospatial visual interfaces, and can also involve human assessment as a final step after automated prefiltering. For example, automated methods to extract and visualize high-volume trajectories from GPS data have been proposed  (Andrienko et al. , 2007). Both two-dimensional (2D) map layouts and 3D visualization based on time geography are powerful approaches to analyzing human movements in geographical space  (Çöltekin et al.
, 2018). Contemporary big data may generate more knowledge than that of the social dimension mentioned above. An undeniable trend is that we have more opportunities to simultaneously observe both geographical and cyber systems. For example, in mobile phone data, visited websites are recorded along with geographical user positions. Thus, it is possible to gather high-resolution trajectories in both geographical space and cyberspace which may, in turn, improve current geovisual frameworks. Empirical research on how to properly define and present entities and personalize or customize visualizations to enhance the geo-visual explainability and amplify human cognition based on audience and context is still required.
It is envisioned that there is an unlimited number of users and continuous dynamic geospatial data in the metaverse, which can as such create virtual spaces whose dynamic and semantic properties can be then explored and analyzed by GeoVA and GeoAI capabilities as we do in real-world geographical spaces  (Ball, 2022). Therefore, the metaverse can be considered a virtual world based on geographical and collaborative cyberspace, where GeoVA can also be used to display and reason with geospatial data in real-time by users who can contact, interact with, and collaborate with each other. As with all the geospatial sciences, earth sciences also benefit from the above-mentioned new paradigms (e. g. ,  Gerloni et al. , 2018 , Moysey and Lazar, 2019 , and Harknett et al. , 2022). For example, mathematical and geoscience models, algorithms, and simulations can be shared in a lively and engaging way using dynamic interfaces and cyberinfrastructures, providing interaction capabilities and favoring methodology exchanges and replicability throughout different experts and disciplines  (Shao et al.
In urban environments, these big data sources can support the development of real-time data observation and processing applications at fine resolution and acceptable accuracy. By integrating multi-source heterogeneous data, GeoAI might favor the development of analysis, modeling, understanding, simulation, and prediction mechanisms that will take full advantage of the data sources generated in geographical space and cyberspace. GeoVA surpasses pictorial representations in visual effects as well as increases the quality and efficiency of understanding the geospatial data along with decisionmaking  (Harbola and Coors, 2018). Utilizing GeoAI to reveal hidden patterns of big data of transportation networks and combining GeoVA to assist transportation management departments in understanding realtime traffic conditions can effectively achieve traffic monitoring in smart cities  (Mortaheb and Jankowski, 2023).
GeoAI applications to novel sources of spatial big data, such as social media, and electronic health personal sensors, have already advanced the science of population health in a significant way  (Mohapatra and Mohanty, 2022). For instance, the automated HealthMap developed by the Boston Children's Hospital scans online and georeferenced news and social media reports for early warning signs of outbreaks. The system continuously (24/7) brings together distinct data sources to integrate, monitor, organize, and visualize online information about emerging diseases and facilitate their early detection  (Cho, 2020). Besides, the mobile communication data of patients with infectious diseases can be well analyzed by GeoAI and an epidemic spread map can be produced and displayed by GeoVA, which can effectively assist government departments in early warning of epidemic transmission and epidemic prevention.
, 2019)  can effectively realize the mapping from geographical space to cyberspace. The mapping from geographical space to cyberspace can help scholars more easily perceive, recognize, reason, and analyze the global forests, water resources, cultivated lands, cities, carbon fluxes and stocks, biodiversity, and human health conditions in combination with the models and methods of GeoAI and GeoVA, so as to obtain knowledge about the distribution of the global poor population, migration rates of wild animals, global forest area change, climate change, and so on. These results can provide geographical science decision-making support for the realization of the 2030 Sustainable Development Goals of the United Nations. While geographical space and cyberspace are likely to provide an extraordinary range of application opportunities, cyberspace also brings avenues for cyber warfare, which can both disrupt cyber data infrastructures as well as the availability and security of critical data  (Carlos Pedro, 2019).
We should not underestimate the difficulties that await us. There is also a risk associated with the complexity of GeoAI and the wide range of possible human interactions which are likely to generate technical dead-ends as the level of expected maturity of many of the emerging application domains are still not well developed. This is likely to result in major difficulties associated with the explanation of data manipulation processes applied to geographical space and cyberspace and it will entail transparency challenges related to the explanation of so-called "black box" computational processes. Personal privacy and data access rights should be preserved and guaranteed by appropriate legal and technical mechanisms, and this at the global and individual levels, as the range of possible interactions in space and time is likely to generate data lakes where humans are likely to act upon these integrated geographical spaces and cyberspace, thus offering many opportunities for malicious actors to infer valuable information on the way humans behave in space and time.
For urban geography, hybrid models and analysis-based cyber-physical frameworks fit the complexity of reality and may result in superior prediction of mobility dynamics and enhanced interpretation of the functionality of urban places  (Qian et al. , 2020). This paper demonstrates how a re-thinking of GIScience, especially about GeoAI and GeoVA, from a cyberspace perspective, will profoundly motivate the innovation of theories and methods in traditional GIScience, as well as promoting interdisciplinary research, e. g. , connecting to fundamental questions in geospatial knowledge acquisition and cognition, and extended (virtual, augmented, mixed) reality. We have provided a brief overview of geospatial big data here and the associated challenges from a cyberspace perspective with a specific focus on GeoAI and GeoVA.
To keep pace with developments in the Fourth Industrial Revolution, we call here for research attention to be focused on the GIScience research agenda from a cyberspace perspective. The more pressing needs include an appropriate ontology for geospatial data related to cyberspace, proposals outlining new virtual GIS capabilities, and the development of applications of GeoAI and GeoVA in cyberspace. We have supported our discussion with provocative and constructive ideas that will stimulate future work.
Business failure prediction (BFP) is an important instrument in assessing the risk of corporate failure. While a large body of research has focused on BFP, recent research in operations research and analytics acknowledges the beneficial effect of incorporating textual data for predictive modelling. However, extant BFP research that incorporates textual company information is very scarce. Based on a dataset containing 13,571 European companies provided by the largest European data aggregator, this study investigates the added value of extending traditional BFP models with textual website content. We further benchmark various feature extraction techniques in natural language processing (i. e. the vector-space approach, neural networks-based approaches and transformers) and assess the best way of representing and integrating textual website features for BFP modelling.
, 2015; Mai et al. , 2019). BFP researchers have established best practices in the choice of prediction models and input data sources in order to optimize predictive performance and derive valuable insights. Specifically, structured (or tabular) input data sources have been extended beyond financial ratios extracted from company disclosures in an attempt to better predict failing companies. Additional input data sources for model inputs include firmographics, payment timeliness measures, and (macro-)economic data  (Shumway, 2001; Doumpos et al. , 2017; De Bock et al. , 2020). Besides these structured input data sources, unstructured textual information extracted from financial disclosures have been introduced to the BFP literature  (Cecchini et al. , 2010; Mai et al. , 2019). These studies explored the integration of textual content extracted from periodic financial statements or 10-K disclosures as BFP input data.
However, these inputs heavily limit the scope of BFP modelling to large enterprises with a public listing given that these companies are obliged to consistently and periodically report financial disclosures to the authorities. Moreover, there are inconsistent disclosure regulations for small-and medium-sized enterprises across countries. Therefore, this study explores the added value of companies' textual website content for BFP models for following two reasons. First, website information broadens the scope of BFP modelling from large to small-and medium-sized companies. The analysis of a company's website content has proven to be a valuable input data source for other financial applications  (Allport & Pendley, 2010; Bollen et al. , 2006; Geerings et al. , 2003) , and it has been successfully used as an input data source for prediction models in other domains (e.
g. Thorleuchter & Van Den Poel (2012)  analyze website content to predict e-commerce firm's success). Second, along with financial information, company websites contain a variety of nonfinancial content ranging from product descriptions, news, marketing-specific content and general company profile information. This diversity in available textual content makes the website an inherently rich input data source and complementary to traditional structured financial input data. BFP models serve a dual purpose for decision makers, i. e. detecting companies that are at risk and helping to understand why firms are at risk. Therefore, predictive performance is not the only requirement for BFP as it should also provide output that can help decision makers in their understanding of why firms are at risk of failure  (Du Jardin, 2019).
In line with recent attempts to generate insights from unstructured data  (Mai et al. , 2019; Ahmadi et al. , 2018) , our study explores what textual information on a firm's website is most relevant for predicting business failure and what insights can be distilled from it. Based on a dataset containing 13,571 European companies, we predict business failure and explore the beneficial impact of website content. In order to extract the most valuable features from a company's textual website content, we benchmark various natural language processing (NLP) techniques. With the aim of enriching the text representations of traditional approaches and capturing semantic context of words more accurately, (deep) neural networks and transformer approaches for textual feature extraction showcase competitive results in recent studies.
First, we explore the added value of the company's website content as a new source of unstructured, textual data for BFP. As a public data source, websites are widely and freely available regardless of the company size or whether the company is publicly listed. This extends the BFP scope of considered companies from large, publicly listed companies to small-and medium-sized enterprises. Moreover, the company website -as compared to other textual data sources -also contains a wide range of non-financial information that extend the perspective of traditional data sources when assessing a firm's health. Second, this study also contributes to literature by introducing a transformer model for BFP by incorporating BERT to extract textual features  (Devlin et al. , 2018). In comparison to shallow neural networks, transformer-based models utilize context dependent word representations and rank high in recent NLP benchmarks.
Related literature This section discusses the application of BFP models. An overview of recent BFP papers, including the ones that use unstructured textual data, is displayed in Table  1. Table  1  sheds light on the type of unstructured textual data sources employed, as well as the NLP techniques for feature extraction used. For a more comprehensive overview of statistical and machine learning models applied in BFP literature, we kindly refer you to  (Kumar & Ravi, 2007; Balcaen & Ooghe, 2006). Table  1  reveals that structured data sources are continuously extended with validating data linked to the external industry and macroeconomic environment as important inputs for BFP models  (Balcaen & Ooghe, 2006). For example,  Doumpos et al. (2017)  find country-level macroeconomic and sector-specific data to improve predictive model performance.
Methodology This section focuses on NLP techniques to incorporate textual data into BFP models. In order to incorporate the textual data in a classification model, textual features are extracted from the numerical representation of the text. The structured and textual features are processed separately before combining them in the classification model inputs. This requires the extracted textual features matrix to match the representation of the structured data on the company level with n being the number of companies in the dataset. Therefore, the textual data extracted from the website is aggregated on company level for subsequent analysis. A detailed overview of combining the textual with structured features is provided in Section 4. 1. An overview of the structured features including the accounting ratios, payment timeliness metrics and firmographics is included in Appendix A1.
In addition to the word vectors used in the prediction task, a paragraph vector is added for the respective document. The unique paragraph vectors are then used as a numeric feature representation of the documents and are obtained by inferencing the trained model. Since the training process aggregates similar words by assigning similar vector representations, the textual data requires fewer preprocessing steps. First, the punctuation and non-alphabetic characters are removed from the corpus. The words are converted in lower case and filtered based on the predefined list of stop words.
Data A global B2B data aggregator provided the dataset for this study consisting of 13,571 European companies which periodically disclose to the National Bank of Belgium (NBB). The dataset has a business failure incidence of 1. 81% or 246 failed companies, and comprises a total of 93,886 web pages. The dataset includes publicly traded companies as well as small-and medium-sized enterprises, whereby the range and granularity of disclosed metrics differ according to size criteria specified by the NBB. The criteria are tied to thresholds regarding the number of employees, revenue, total assets and ownership structures of the companies which also determine the applied model structure (i. e. full, shortened or micro model)  (National Bank of Belgium, 2015). The dataset consists of 1,791 large, 7,579 small and 4,201 micro companies according to NBB size criteria.
With careful alignment of the website snapshots, the recency of the textual content included in the analysis is dependent on the frequency company websites are kept up to date. The binary target variable is created in line with existing literature and is based on a company failing in 2019, which includes the bankruptcy and insolvency event  (Du Jardin, 2019). In order to treat outliers, winsorization is applied on continuous structured variables in line with  Mai et al. (2019). The maximum value of each variable is set to the 97. 5th percentile value, while all observations exceeding this limit are replaced with the respective maximum value. Similarly, observations lower than the 2. 5th percentile value are replaced by the lower boundary. Along with structured variables extracted from the periodic disclosures to the NBB, the dataset is enriched with textual content extracted from the company website.
Starting from the homepage, all subsequent links with the same domain title are included in the textual dataset resulting in a total of 93,886 web pages. This might include a variety of information sources ranging from general company descriptions, product information, news articles or investor relation sites. Despite possible differences in terms of website content characteristics, we opt to keep the extraction procedure consistent for all companies to maintain generalizability. Embedded files on the web pages like PDF reports, presentations, photos, or videos are excluded from the extraction process. All web pages of the same company are aggregated before the preprocessing steps are applied. The specific hyperparameter settings of the NLP techniques included in the benchmark are displayed in Appendix A2.
We explore the application of the fine-tuned BERT model on the BFP task, by training it on the textual website data provided in the dataset. This study considers the BERT-Base model developed by  Devlin et al. (2018). The BERT Base model uses a 12-layer architecture and a maximum input size of k = 512 tokens per document. We access the output of the last BERT layer and apply max pooling to incorporate the resulting d BERT = 768 output dimensions with the structured data in the classification model. In order to preprocess the text data and transform it into a format compatible with the BERT Base model, documents longer than 512 tokens are truncated, while shorter documents are padded with zeros. With a median document length of 335 words in the dataset, most documents are padded with zeros during preprocessing.
BFP models and evaluation This study considers logistic regression (LR), XGBoost (XGB) and multilayer perceptron (MLP) as base models based on their popularity and good performance in the BFP domain. In line with the research objectives, we report the best performing base model for each NLP technique based on its cross-validated performance on the holdout set. As an additional benchmark, we include the table summarizing the evaluation results for all base models and NLP techniques in Appendix A4. LR is a popular classification model in most BFP studies due to its competitive predictive performance and its ease of use in business  (Doumpos et al. , 2017). Two additional preprocessing steps are considered for LR. First, for the structured data input, we employ a variable selection approach based on the T-test (continuous variables) and Chi-squared test (categorical variables) using a five percent significance threshold in line with  Tsai (2008).
, 2014). The Adamax optimizer is a variant of the Adam optimizer based on the infinity norm  (Kingma & Lei Ba, 2015). For structured inputs of the MLP classification models, the data is centered and scaled  (LeCun et al. , 1998; Mai et al. , 2019). In order to emulate a standard normal distribution, the mean (x) is subtracted from each observation (x n ) and divided by the standard deviation (σ). Each computation is executed on a feature-wise basis. This results in a distribution with zero mean and a variance of one. A 5x2-fold cross-validation is used in this study  (Dietterich, 1998) , where per fold the dataset is split in two equally-sized parts, i. We compute an F-statistic according to  Alpaydin (1999) : f = 5 i=1 2 j=1 (p (j) i ) 2 2 5 i=1 s 2 i (3) The test statistic is approximately F-distributed with 10 and 5 degrees of freedom, where p (j) i is the difference between performance metrics of two classification models and s 2 i is the estimated variance  (Alpaydin, 1999).
In line with previous research, the mean average precision (MAP), F1-score and receiver operating characteristics curve (AUC) are used as evaluation criteria  (Dimitras et al. , 1996; Antunes et al. , 2017). The average precision is a cutoff independent metric summarizing the precision-recall curve for each threshold value: AP = j i=1 (Recall i -Recall i-1 ) * P recision i ( 4 ) where j is the number of threshold values considered in the precision-recall curve. The MAP value is obtained by averaging the average precision over the number of iterations, i. e. the number of predictions on the holdout set or 10 according to the 5x2-fold cross-validation  (Dietterich, 1998) : M AP = k i=1 AP i k (5) The F1-score is the harmonic mean of precision and recall, which is particularly useful for evaluating imbalanced BFP datasets.