Machine-readable Format Research Articles

Grading assistance for a handwritten thermodynamics exam using artificial intelligence: An exploratory study

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Using a high-stakes thermodynamics exam as the sample (252 students, four multipart problems), we investigate the viability of four workflows for AI-assisted grading of handwritten student solutions. We find that the greatest challenge lies in converting handwritten answers into a machine-readable format. The granularity of grading criteria also influences grading performance: employing a fine-grained rubric for entire problems often leads to errors and grading failures, as the model appears to be unable to keep track of scores for more than a handful of rubric items, while grading problems in parts is more reliable but tends to miss nuances. We also found that grading hand-drawn graphics, such as process diagrams, is less reliable than mathematical derivations due to the difficulty in differentiating essential details from extraneous information. Although the system is precise in identifying exams that meet passing criteria, exams with failing grades still require human grading. We conclude with recommendations to overcome some of the encountered challenges. Published by the American Physical Society 2024

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

What is in your cookie box? Explaining ingredients of web cookies with knowledge graphs

The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.

RWTH-DBIS at LLMs4OL 2024 Tasks A and B

The increasing capabilities of Large Language Models (LLMs) have opened new opportunities for enhancing Ontology Learning (OL), a process crucial for structuring domain knowledge in a machine-readable format. This paper reports on the participation of the RWTH-DBIS team in the LLMs4OL Challenge at ISWC 2024, addressing two primary tasks: term typing and taxonomy discovery. We used LLaMA-3-8B and GPT-3.5-Turbo models to find the performance gaps between open-source and commercial LLMs. For open-source LLMs, our methods included domain-specific continual training, fine-tuning, and knowledge-enhanced prompt-tuning. These approaches were evaluated on the benchmark datasets from the challenge, i.e., GeoNames, UMLS, Schema.org, and the Gene Ontology (GO), among others. The results indicate that domain-specific continual training followed by task-specific fine-tuning enhances the performance of open-source LLMs in these tasks. However, performance gaps remain when compared to commercial LLMs. Additionally, the developed prompting strategies demonstrate substantial utility. This research highlights the potential of LLMs to automate and improve the OL process, offering insights into effective methodologies for future developments in this field.

IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions.

The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is a collaborative effort with a central mission of aggregating detailed molecular interaction experimental evidence in a machine-readable format, supported by controlled vocabularies and standard ontologies. The IntAct molecular interaction database (www.ebi.ac.uk/intact), as an IMEx partner, serves as a valuable portal for accessing IMEx data through user-friendly search options and an array of interactive filters. The resource currently hosts an extensive repository of 1,293,508 binary interactions meticulously captured from 75,098 experiments documented in 23,366 publications (as of the February 2024 release), with this corpora being added to by regular data releases. IMEx curation policy has consistently prioritized a fine-grained data and curation model, with a focus on capturing the relevant experimental details essential for interpreting molecular interaction data effectively. Our curation process is designed to support the generation of interactomes tailored to contexts such as disease-specific or tissue-/cell-type-specific interactomes. These interactions are ranked according to a scoring system based on the Proteomics Standard Initiative Molecular Interaction (PSI MI) standards. This scoring system allows users to assess the degree of confidence in binary interactions, enhancing the value of the data. The resource provides insights into the nature of relationships among interacting partners as defined by the experimental setup and the associated biological context. Interactive filters enable users to navigate these rich, multilayered data, promoting a deeper understanding of biological complexity. Additionally, the IntAct website fosters the creation of networks for collaborative analyses by the scientific community. The recent transformation of the IntAct website, supported by a graph-type database, empowers users to execute custom queries tailored to their specific research interests. This article illustrates the diverse levels of annotations available for interactions and the multiple search options at users' disposal to access data of interest. © 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using Quick Search, network visualization, and filters Support Protocol: Accessing fine annotations from intact: Unlocking the molecular details Alternate Protocol: Using batch search: Querying multiple interactors Basic Protocol 2: Using advanced search: Precision and customization.

Re-imagen: Generating coherent background activity in synthetic scenario-based forensic datasets using large language models

Due to legal and privacy-related restrictions, the generation of synthetic data is recommended for creating datasets for digital forensic education and training. One challenge when synthesizing scenario-based forensic data is the creation of coherent background activity besides evidential actions. This work leverages the creative writing abilities of large language models (LLMs) to generate personas and actions that describe the background usage of a device consistent with the created persona. These actions are subsequently converted into a machine-readable format and executed on a virtualized device using VM control automation. We introduce Re-imagen, a framework that combines state-of-the-art LLMs and a recent unintrusive GUI automation tool to produce synthetic disk images that contain arguably coherent “wear-and-tear” artifacts that current synthesis platforms lack. While, for now, the focus is on the coherence of the generated background activity, we believe that the proposed approach is a step toward more realistic synthetic disk image generation.

Requirements for Playbook-Assisted Cyber Incident Response, Reporting and Automation

Cybersecurity playbooks assume an increasingly important role as threat-specific documents for guiding operators in the context of cyber incident response. However, these playbooks are mostly unstructured or semi-structured, which significantly limits their utility when it comes to automating response and reporting steps, complying with cybersecurity directives, or sharing best practices for incident response across organisations. We thus argue that cybersecurity playbooks must transition to interoperable and machine-readable formats from generation, via management and utilisation to cross-organisational sharing. In this work, we identify and structure key requirements based on expert interviews as a first step toward this transition. From these requirements, we derive a framework for further guidance during the transition to structured security playbooks and their utilisation in a tool-assisted fashion. We discuss the implications of our framework and lessons learned before outlining directions for future research.

Innovating Pharmaceuticals: The Rise of 3D printing in Drug Delivery.

Additionally known as three-dimensional (3D) printing, additive manufacturing has made major advancements possible in the fields of engineering, business, the arts, education, and medical. Thanks to recent developments, it is now possible to print three-dimensional to create complex, useful living tissues, biocompatible substances, cells, and supporting structures are combined. Regenerative medicine is utilizing 3D bioprinting. Additive manufacturing technology, or 3D printing, has been labelled the “next big thing” and is predicted to overtake cell phones in popularity. 3D printers use digital templates to produce actual, three-dimensional items. Adding layers to a print, commonly referred to as additive manufacturing, allows for the use of more than a hundred different materials, including nylon, metal, and plastic. Applications for 3D printing can be found in many different industries, such as industrial design, manufacturing, dental, automotive, aerospace, civil engineering, education, jewellery, footwear, and geographic information systems. It has shown to be a simple and affordable solution for a variety of use cases. Using computer-aided design tools and programming, three-dimensional printing is a sophisticated technique that adds material to a base surface to create three-dimensional things. Additive layer manufacturing, also referred to as 3D printing, is the technique of creating three-dimensional things by depositing or solidifying material one layer at a time. Using a computer-aided design module, pharmaceutical components are organized in a three-dimensional pattern. Afterwards, the constituents are converted into a machine-readable format resembling the surface of a three-dimensional dosage form. 3D printing has been used for jewelry, shoe-making, architecture, engineering & construction, the automotive industry as well as the aerospace field, dentistry and medicine, plus geographic information systems (GIS), civil engineering and education. After that stage of the process is completed, the surface transferred to the machine is then printed in different layers. Bioprinting is an interdisciplinary domain that integrates additive manufacturing with biology and material sciences to manufacture threedimensional structures representative of living organisms. The ability to create biological tissues and organs has attracted considerable attention in biomedical research owing to the rising demand for personalized medicine. This scenario propelled bioprinting forward which received much interest thus triggering comprehensive research efforts by various players such as companies, universities as well as research institutes. The goal of this book is to provide a thorough analysis of the complex and rapidly evolving field of bioprinting by critically analyzing and evaluating the existing scientific literature.

Gly2Mdc v.2.0

In order to advance digital methods in Egyptology, machine-readable hieroglyphic texts are needed. While machine-readable cuneiform texts have been extensively employed in Assyriological studies, the intricate nature of hieroglyphic script poses challenges in creating accessible corpora. Specific hieroglyphic text editors are used to produce pictures of the texts with signs placed correctly above and next to each other in kind of boxes. The pictures are used in publications, but the machine-readable project files are generally discarded. In this paper, I introduce Gly2Mdc v.2.0, a tool designed to transform the .gly files containing encoded hieroglyphic texts into a more human-readable format. The tool extracts and cleans the encoding and offers users options for saving the text in different formats. The aim is to give the users of hieroglyphic text editors a chance to publish the text also in machine-readable format and increase the amount of text available for building digital methods. Challenges faced in developing this tool are discussed, including the impossibility of achieving a faithful rendition of the original text in machine-readable form and the challenges of converting encoding to Unicode.

GIATAR: a Spatio-temporal Dataset of Global Invasive and Alien Species and their Traits

Monitoring and managing the global spread of invasive and alien species requires accurate spatiotemporal records of species presence and information about the biological characteristics of species of interest including life cycle information, biotic and abiotic constraints and pathways of spread. The Global Invasive and Alien Traits And Records (GIATAR) dataset provides consolidated dated records of invasive and alien presence at the country-scale combined with a suite of biological information about pests of interest in a standardized, machine-readable format. We provide dated presence records for 46,666 alien taxa in 249 countries constituting 827,300 country-taxon pairs in locations where the taxon’s invasive status is either alien, invasive, or unknown, joined with additional biological information for thousands of taxa. GIATAR is designed to be quickly updateable with future data and easy to integrate into ongoing research on global patterns of alien species movement using scripts provided to query and analyze data. GIATAR provides crucial data needed for researchers and policymakers to compare global invasion trends across a wide range of taxa.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

ENTRANT: A Large Financial Dataset for Table Understanding

Tabular data is a way to structure, organize, and present information conveniently and effectively. Real-world tables present data in two dimensions by arranging cells in matrices that summarize information and facilitate side-by-side comparisons. Recent research efforts aim to train large models to understand structured tables, a process that enables knowledge transfer in various downstream tasks. Model pre-training, though, requires large datasets, conveniently formatted to reflect cell and table characteristics. This paper presents ENTRANT, a financial dataset that comprises millions of tables, which are transformed to reflect cell attributes, as well as positional and hierarchical information. Hence, they facilitate, among other things, pre-training tasks for table understanding with deep learning methods. The dataset provides table and cell information along with the corresponding metadata in a machine-readable format. We have automated all data processing and curation and technically validated the dataset through unit testing of high code coverage. Finally, we demonstrate the use of the dataset in a pre-training task of a state-of-the-art model, which we use for downstream cell classification.

IrCytoToxDB: a dataset of iridium(III) complexes cytotoxicities against various cell lines

Iridium(III) complexes nowadays became rising stars in various health-related applications. Thus, there is a necessity to assess cytotoxicity of the synthesized molecules against cancer/normal cell lines. In this report, we present a dataset of 2694 experimental cytotoxicity values of 803 iridium complexes against 127 different cell lines. We specify the experimental conditions and provide representation of the complexes molecules in machine-readable format. The dataset provides a starting point for exploration of new iridium-based cellular probes and opens new possibilities for predictions of toxicities and data-driven generation of new organometallic anticancer agents.

A computation of two-loop six-point Feynman integrals in dimensional regularization

We compute three families of two-loop six-point massless Feynman integrals in dimensional regularization, namely the double-box, the pentagon-triangle, and the hegaxon-bubble family. This constitutes the first analytic computation of two-loop master integrals with eight scales. We use the method of canonical differential equations. We describe the corresponding integral basis with uniform transcendentality, the relevant function alphabet, and analytic boundary values at a particular point in the Euclidean region up to the fourth order in the regularization parameter ϵ. The results are expressed as one-fold integrals over classical polylogarithms. We provide a set of supplementary files containing our results in machine-readable form, including a proof-of-concept implementation for numerical evaluations of the one-fold integrals valid within a subset of the Euclidean region.

Computational Argumentation-based Chatbots: A Survey

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

ChemReco: automated recognition of hand-drawn carbon–hydrogen–oxygen structures using deep learning

Chemical molecular structures are a direct and convenient means of expressing chemical knowledge, playing a vital role in academic communication. In chemistry, hand drawing is a common task for students and researchers. If we can convert hand-drawn chemical molecular structures into machine-readable formats, like SMILES encoding, computers can efficiently process and analyze these structures, significantly enhancing the efficiency of chemical research. Furthermore, with the progress of educational technology, automated grading is gaining popularity. When machines automatically recognize chemical molecular structures and assess the correctness of the drawings, it offers great convenience to teachers. We created ChemReco, a tool designed to identify chemical molecular structures involving three atoms: C, H, and O, providing convenience for chemical researchers. Currently, there are limited studies on hand-drawn chemical molecular structures. Therefore, the primary focus of this paper is constructing datasets. We propose a synthetic image method to rapidly generate images resembling hand-drawn chemical molecular structures, enhancing dataset acquisition efficiency. Regarding model selection, the hand-drawn chemical molecule structural recognition model developed in this article achieves a final recognition accuracy of 96.90%. This model employs the encoder-decoder architecture of EfficientNet + Transformer, demonstrating superior performance compared to other encoder-decoder combinations.

MPOCSR: optical chemical structure recognition based on multi-path Vision Transformer

AbstractOptical chemical structure recognition (OCSR) is a fundamental and crucial task in the field of chemistry, which aims at transforming intricate chemical structure images into machine-readable formats. Current deep learning-based OCSR methods typically use image feature extractors to extract visual features and employ encoder-decoder architectures for chemical structure recognition. However, the performance of these methods is limited by their image feature extractors and the class imbalance of elements in chemical structure representation. This paper proposes MPOCSR (multi-path optical chemical structure recognition), which introduces the multi-path Vision Transformer (MPViT) and the class-balanced (CB) loss function to address these two challenges. MPOCSR uses MPViT as an image feature extractor, combining the advantages of convolutional neural networks and Vision Transformers. This strategy enables the provision of richer visual information for subsequent decoding processes. Furthermore, MPOCSR incorporates CB loss function to rebalance the loss weights among different categories. For training and validation of our method, we constructed a dataset that includes both Markush and non-Markush structures. Experimental results show that MPOCSR achieves an accuracy of 90.95% on the test set, surpassing other existing methods.

Theoretical and Legal Characteristics of Machine-Readable Law

The idea of automating law and legal activity has become increasingly popular in the scientific legal environment in recent years. One of the ways of such automation is to transform legislation into a machine-readable form. This could provide the possibility of automatic application of the law, increase legal certainty, and make the law accessible to the user. The adoption in 2021 of the Concept for the Development of Machine-Readable Law Technologies became one of important steps towards the introduction of machine-readable law in Russia. It seems that for the successful development of this area, we need a comprehensive theoretical and legal understanding of a new phenomenon for the domestic legal theory, namely: machine-readable law. Using formal logical methods, the author gives a definition of machine-readable law based on the essential and necessary features of the phenomenon under consideration. The paper provides a list of mandatory and optional features of machine-readable law. Special attention is paid to the issues of algorithmization of law and the use of legal ontologies. The paper highlights the main consequences of machine-readable law relating both to the law-making stage and to the stage of using machine-readable law. Such consequences include various types of testability of machine-readable law, the possibility of automatic interpretation, enforcement and direct implementation of machine-readable legal regulations. The author provides the concept of machine-readable law in the broadest sense of the word, covering not only legal regulations, but also the content of smart contracts, machine-readable powers of attorney. The paper also deals with the issues of machine-executable law and the related transition from legal normativity to technological normativity. This work contributes to the theoretical and legal understanding of machine-readable law and can contribute to its successful development in Russia.

Spectral Energy Distribution of Late Stage Stars

This paper presents a catalog of the energy distribution in the spectra of 263 stars in the wavelength range 0.4 - 100 µm, which are at late stages of evolution and have been observed by the ISO space observatory. For each object in the catalog, estimates of the observed bolometric fluxes are derived from smoothed energy distribution curves. The catalog is available at https://infra.sai.msu.ru/sai_lss_sed both as a table and in machine-readable format. It is shown that for the specified sample of objects their ISO SWS spectra in the range 2.4 - 45 µm only in 60% of cases correspond to the general shape of the continuum, and can be used without recalibration. A selection of carbon stars accessible to infrared observations from the MSU observatories was made. For some of them, the first brightness estimates in the K, L, and M bands were obtained with the new IR camera of the 2.5-m telescope of CMO.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives.

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.