• Digital Humanities and Cultural Heritage
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• 2014-2023close
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An R 'Shiny' application for the visualisation, interactive exploration, and web communication of archaeological spatial data. It includes interactive 3D and 2D visualisations, generation of cross sections and maps of the remains, basic spatial analysis methods (convex hull, regression surfaces, 2D kernel density estimation), and excavation timeline visualisation. 'archeoViz' can be used locally or deployed on a server, either with interactive input of data or with a static data set.

We present small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python. It features many pre-implemented state-of-the-art query strategies, including some that leverage the GPU. Standardized interfaces allow the combination of a variety of classifiers, query strategies, and stopping criteria, facilitating a quick mix and match, and enabling a rapid development of both active learning experiments and applications. To make various classifiers and query strategies accessible in a unified way, small-text integrates the well-known machine learning libraries scikit-learn, PyTorch, and huggingface transformers. The latter integrations are available as optionally installable extensions, making the availability of a GPU competely optional. The library is publicly available under the MIT License at https://github.com/webis-de/small-text. {"references": ["Christopher Schr\u00f6der, Lydia M\u00fcller, Andreas Niekler, and Martin Potthast. 2021. Small-Text: Active Learning for Text Classification in Python. arXiv preprint arXiv:2107.10314."]}

We acknowledge the CERIC-ERIC Consortium for the access to experimental facility SYRMEP of ELETTRA and financial support for synchrotron measurements. Repository of the image processing software for the CERIC synchrotron X-ray micro Computed Tomography beamtime proposal 20217193.

The Active learning for Systematic Reviews (ASReview) project implements learning algorithms that interactively query the researcher. This way of interactive training is known as Active Learning. ASReview offers support for classical learning algorithms and state-of-the-art learning algorithms like neural networks. ASReview LAB is the graphical user interface of the open-source research software and ships with an Oracle, Exploration and Simulation Mode.

Data and R script accompanying article in Journal of Archaeological Method and Theory. Estimation of fractal dimension and lacunarity on empirical (Linear Pottery and Trypillia/European Neolithic) and simulated settlement plans through box-counting and gliding box-algorithms. File includes binary images of all plans that are analysed in the article (N=70). Written as part of the author's PhD thesis at the University of Oslo. See README file (included) and main article for further description and details. Version 2 after peer review: two additional lacunarity parameters, two removed residual parameters, updated plots.

ConfigILM is an open-source Python library for rapid iterative development of image-language models in pytorch. It provides a convenient implementation for seamlessly combining models from two popular pytorch libraries, timm and huggingface . This allows a variety of configurations of models without additional implementation effort. At the same time, the interface simplifies the exchange of components of the model and thus offers development possibilities for novel models. In addition, the package provides pre-built and throughput-optimized pytorch dataloaders, allowing developed models to be tested directly in various application areas such as remote sensing (RS) or common objects in context (COCO). The documentation contains installation instructions, tutorial examples, and a complete discussion of the interface to the library. ConfigILM is released under the MIT License, thus encouraging its use in academic and commercial environments. Source code and documentation are available at https://github.com/lhackel-tub/ConfigILM

ConfigILM is an open-source Python library for rapid iterative development of image-language models in pytorch. It provides a convenient implementation for seamlessly combining models from two popular pytorch libraries, timm and huggingface . This allows a variety of configurations of models without additional implementation effort. At the same time, the interface simplifies the exchange of components of the model and thus offers development possibilities for novel models. In addition, the package provides pre-built and throughput-optimized pytorch dataloaders, allowing developed models to be tested directly in various application areas such as remote sensing (RS) or common objects in context (COCO). The documentation contains installation instructions, tutorial examples, and a complete discussion of the interface to the library. ConfigILM is released under the MIT License, thus encouraging its use in academic and commercial environments. Source code and documentation are available at https://github.com/lhackel-tub/ConfigILM If you use this software, please cite it using the metadata from this file.

DeBEIR is a library aimed at helping practitioners, researchers and data scientists experimenting with bi-encoders by providing them with dense retrieval methods that are easy to use out of the box but also have additional extendability for more nuanced research. Our pipeline runs asynchronously to reduce I/O performance bottlenecks, facilitating faster experiments and research.