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52 results for “AI Research”
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </p>
A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)
<p><strong>A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)</strong></p> <p>This controlled vocabulary of keywords related to the field of Artificial Intelligence (AI) was built by SIRIS Academic in collaboration with ART-ER (the R&I and sustainable development in-house agency of the Emilia-Romagna region in Italy) and the Generalitat de Catalunya (the regional government of Catalonia, Spain), in order to identify AI research, development and innovation activities. The work was carried out by consulting domain experts' advice and it was ultimately applied to inform regional strategies on AI and research and innovation policy.</p> <p>The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of AI.</p> <p>The present effort was carried out because, despite the high number of contributions and technological developments in the field of AI, there is no closed or static vocabulary of concepts that allows to unequivocally define the boundaries of what should be considered “an Artificial Intelligence intellectual product” (or what should not). Indeed, the literature presents different definitions of the domain, with visions that could be contradictory. AI encompasses today a wide variety of subdomains, ranging from general purpose areas such as learning and perception to more specific ones such as autonomous vehicle driving, theorem proving, or industrial process monitoring. AI synthesises and automates intellectual tasks, and is therefore potentially relevant to any area of human intellectual activity. In this sense, it is a genuinely universal and multidisciplinary field. AI draws upon disciplines as diverse as cybernetics, mathematics, philosophy, sociology and economics.</p> <p>As a ground for the construction of the AI controlled vocabulary, an initial set of concepts was taken from different subdomains of the <em>ACM Computing Classification System 2012, </em> to define the boundaries of the AI domain. Notably, although some relevant AI subdomains have an independent category in the ACM taxonomy outside of AI, they have been included in the list of subdomains. In order to align the ACM taxonomical definition with the Catalan Strategy of AI, <em>CATALONIA.AI</em>, in <em>version 1 </em>of this resource the emerging area of AI Ethics was included in the vocabulary, while some other categories which are not relevant for the objectives were removed from the subdomains list. In the current <em>version 2</em>, the classification and the labels of the subdomains have been revised because of the evolution of the field. Some fields have been grouped in order to reduce the overlap between subdomains and to provide a taxonomy that makes more sense for the analysis of R&I ecosystems. </p> <p>The different subdomains in the versions are presented in the following table:</p> <table> <tbody> <tr> <td><strong>Version </strong></td> <td><strong>Subdomains</strong></td> </tr> <tr> <td> <p><em>Version 2</em></p> </td> <td> <p>(1) Machine learning and deep learning; (2) Computer Vision; (3) Natural Language Processing and speech recognition; (4) Intelligent agents, planning, scheduling, problem-solving, control methods, and search; (5) Expert Systems, Knowledge representation and reasoning; (6) AI Ethics.</p> </td> </tr> <tr> <td><em>Version 1</em></td> <td>(1) General, (2) Machine Learning, (3) Computer Vision, (4) Natural Language Processing, (5) Knowledge Representation and Reasoning, (6) Distributed Artificial Intelligence, (7) Expert Systems, Problem-Solving, Control Methods and Search and (8) AI Ethics.</td> </tr> </tbody> </table> <p>Although a keyword rule-based approach suffers from the major shortcomings of not capturing all the lexical and linguistic variants of specific concepts nor the context of the words - namely, keyword-based approaches would miss relevant texts if the specific pattern is not matched during the search - the present vocabulary allowed us to obtain fairly good results, due to the specificity of the concepts describing the AI domain. Furthermore, an understandable and transparent controlled vocabulary allows a better control of the final results and the final definition of the domain borders. Also, a plain list of terms allows a much easier and interactive engagement of interested stakeholders with different degrees of knowledge (such as, for instance, domain experts, policy-makers and potential users) who can make use of vocabulary to retrieve pertinent literature or to enrich the resource itself.</p> <p>The vocabulary has been built taking advantage of advanced language models and resources from knowledge datasets such as arXiv, DBpedia and Wikipedia. The resulting vocabulary comprises 833 keywords, and has been validated by experts from several universities in Emilia-Romagna and Catalonia.</p> <p>The <em>version 0.5</em> of this resource was developed by the SIRIS Academic in 2019 in collaboration with ART-ER, Emilia-Romagna (Quinquillá et <em>al.</em>, 2020), the <em>version 1 </em>was the result of an update done in 2020 in collaboration with the Generalitat de Catalunya, and the current version (<em>version 2</em>) has resulted in 2021 from the collaboration with ART-ER and the integration of an additional set of keywords provided by the <em>Artificial Intelligence and Intelligence Systems (AIIS)</em> Laboratory of the CINI (<em>Consorzio interuniversitario nazionale per l’informatica </em>based in Rome, Italy).</p> <p>The methodology for the construction of the controlled vocabulary is presented in the following steps:</p> <ol> <li> <p>An initial set of scientific publications was collected by retrieving the following records as a weakly-supervised (in the sense that records are linked to AI by their taxonomy and not by a manual label) dataset in the domain of Artificial Intelligence :</p> <ol> <li> <p>Publications from Scopus with the keyword “Artificial Intelligence”</p> </li> <li> <p>Publications from arXiv in the category “Artificial Intelligence”</p> </li> <li> <p>Publications in relevant journals in the scientific domain of “Artificial Intelligence”</p> </li> </ol> </li> <li> <p>An automated algorithm was used to retrieve, from the APIs of DBpedia, a series of terms that have some categorical relationships (i.e. those that are indexed as “sub-categories of”, “equivalent to”, among other relations in DBpedia) with the Artificial Intelligence concept and with the AI categories in the ACM taxonomy. The DBpedia tree has been exploited down to the level 3, and the relevant categories have been manually selected (for instance: <em>Classification algorithms</em>,<em> Machine learning</em> or <em>Evolutionary computation</em>) and others were ignored (for instance: <em>Artificial intelligence in fiction</em>, <em>Robots</em> or <em>History of artificial intelligence</em>) because they were not relevant, or not specifically in the domain.</p> </li> <li> <p>The keywords in publications in the dataset were extracted from the keyword sections and from the abstracts. The keywords with a higher <em>TF-IDF</em>, using an <em>IDF</em> matrix in the open domain, have been selected. The co-occurrence of keywords with categories in specific AI subdomain and a clusterization of the main keywords has been used for a categorization of the keywords at the thematic level.</p> </li> <li> <p>This list of keywords tagged by thematic category has been manually revised, removing the non-pertinent keywords and changing the wrong categorizations by fields.</p> </li> <li> <p>The weak-supervised dataset in the domain of Artificial Intelligence is used to train a Word2Vec (Mikolov <em>et al.</em>, 2013) word embedding model (a machine learning model based on neural networks).</p> </li> <li> <p>The terms’ list is then enriched by means of automatic methods, which are run in parallel: </p> <ol> <li> <p>The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.</p> </li> <li> <p>Further, terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.</p> </li> </ol> </li> <li> <p>The final list produced by steps 2-6 is manually revised.</p> </li> </ol> <p> </p> <p>The definition of the vocabulary does not, per se, allow to identify STI contributions to AI: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated ambiguous keywords.</p> <p>Finally, each keyword in the vocabulary was assigned one or more AI subdomains, so that the vocabulary can also be used to tag collections of texts within narrower AI sub-domains. In order to complement the alignment between keywords and subdomains, a set of subdomain-specific keywords have been defined to better capture the scope of the subdomains. These allow better characterization of subdomains that are more difficult to define only by means of unambiguous specific concepts, or that overlap with the wide “machine learning” subdomain (example: machine learning applied to object recognition or text translation). The alignment between keywords and subdomains, and these keyword lists of each subdomain, have been applied to capture AI subdomains in research outputs. Through this classification process, we have identified projects and publications related to AI, with a focus on mapping the research competencies in the AI domain in Emilia-Romagna. The resulting research records have been reviewed by experts in the domain, given the occurrence of some false positives, which have been used to improve the approach.</p> <p>The final controlled vocabulary has been evaluated with an external test set, proposed by (Dunham <em>et al.,</em> 2020). The test set consists of the abstract of 10,606 papers published in the arXiv repository, of which 1,076 within the Artificial Intelligence subcategories and 9,530 in arXiv categories other than Artificial Intelligence. Evaluating the controlled vocabulary on this data set, we observe accuracy of .94. However, because the pertinence of these publications to the field of AI is based solely on their taxonomic classification (i.e., on whether they are classified in the arXiv within Artificial Intelligence and not on a manual labelling), this evaluation can only yield an orientative performance assessment.</p> <p>The version 2 includes new keywords extracted from the (1) re-training of the enrichment pipeline (steps 5-6 in the methodology) considering as initial set of terms the version 1 of the vocabulary on a reference corpus of new publications, and (2) from the flat keywords list provided by the <em>Artificial Intelligence and Intelligence Systems</em> <em>(AIIS)</em> Lab of CINI (Consorzio interuniversitario nazionale per l’informatica). The keywords in (2) have been cleaned by calculating precision and f-measure on the dataset (Dunham et al., 2020), selecting those keywords with the highest scores, and being manually validated a posteriori.</p> <p>The AI controlled vocabulary has been applied in two practical cases, which have the purpose of identifying skills, stakeholders and capabilities, of a specific research ecosystem at the regional level. See the following references:</p> <ul> <li> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p> </li> <li> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li> <p>Tatiana Fernández (Direcció General de Promoció Econòmica, Competència i Regulació, de la Generalitat de Catalunya), </p> </li> <li> <p>Daniel Marco, Daniel Santanach and Eduard Balbuena (Departament de Polítiques Digitals i Administració Pública, de la Generalitat de Catalunya) </p> </li> <li> <p>Albert Sabater (Observatori d’Ètica en Intel·ligència Artificial i Universitat de Girona)</p> </li> <li> <p>Leda Bologni, Lucia Mazzoni and Giorgio Moretti (Art-ER)</p> </li> <li> <p>Prof. RIta Cucchiara and Dr. Lorenzo Baraldi (Università degli Studi di Modena e Reggio Emilia)</p> </li> <li> <p>Artificial Intelligence and Intelligence Systems (AIIS) Lab of CINI (Consorzio interuniversitario nazionale per l’informatica)</p> </li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> <p>Dunham, J.W., Melot, J., & Murdick, D. (2020). Identifying the Development and Application of Artificial Intelligence in Scientific Text. ArXiv, abs/2002.07143. Available at: <a href="https://arxiv.org/abs/2002.07143">https://arxiv.org/abs/2002.07143</a></p> <p>Mikolov, Tomas & Corrado, G.s & Chen, Kai & Dean, Jeffrey. (2013). Efficient Estimation of Word Representations in Vector Space. 1-12.</p> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
The Why and What of AI Deployment and Innovation in Companies – Results and Learnings from a Systematic Literature Research
<p>These appendices are made available online as a critical part of the research paper accepted for publication at ISPIM 2024 in Tallinn on 10-12June 2024.</p> <p><strong>Abstract:</strong> Despite vast and growing investments in Artificial Intelligence, only a tiny fraction of companies report achieving tangible results. To fill this gap and establish a baseline for companies, we conducted Systematic Literature Research on the status of AI deployment in companies. We analyzed and synthesized 99 peer-reviewed articles for objectives, approaches, results, and learnings. This research confirms that AI is still at the pre-deployment stage in companies. Developing the capability to deploy AI is more challenging and takes more time, concerted effort, innovation, and learning than generally thought. Through the results and the conceptual framework, this research increases understanding of AI deployment and emphasizes the importance of the pre-deployment stages from theoretical and practical viewpoints. The Sensing stage of the dynamic capabilities theory aligns well with the developed pre-deployment stages. More real-life case research is needed, and existing theories verified to deploy AI knowledgeably for business and stakeholder benefits. </p>
Illustrations for AI for multiple long-term conditions: Research Support Facility
<p>Illustrations created by <a href="http://www.scriberia.co.uk/">Scriberia</a> as part of <a href="https://www.turing.ac.uk/research/research-projects/ai-multiple-long-term-conditions-research-support-facility">AI for multiple long-term conditions: Research Support Facility.</a> The archived website link is available <a href="https://web.archive.org/web/20250212145350/https://www.turing.ac.uk/research/research-projects/ai-for-multiple-long-term-conditions-research-support-facility?__cf_chl_rt_tk=XKaDrLiiU8hmzZk2rKaWDBVpnO8exVqC4EHijqlWTIQ-1739372030-1.0.1.1-hmHFksRkdOQcBH9PsOgRVdNAatQdr3i4hFOqtyPcyZU">here</a>.</p> <p>The AIM RSF is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647).</p> <p>When using any of the images, please credit them with</p> <p><em>"This image was created by <a href="http://www.scriberia.co.uk/">Scriberia</a> for AI for multiple long-term conditions: Research Support Facility and is used under a CC-BY licence."</em><br><br>We have created Alternative texts (Alt text) for all images stored in PDF and text format.</p>
Generative AI in Crowdwork for Web and Social Media Research: A Survey of Workers at Three Platforms
<p>Data generated for the paper "<span>Generative AI in Crowdwork for Web and Social Media Research:</span><br><span>A Survey of Workers at Three Platforms</span>" presented at ICWSM 2024.</p>
AMD AI Research Focused on Data Processing Efficiency
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href="https://newspovgamingstyle.blogspot.com/2024/11/intel-ai-advancements-revolutionizing.html">https://newspovgamingstyle.blogspot.com/2024/11/intel-ai-advancements-revolutionizing.html</a> <a href="https://newspovgamingstyle.blogspot.com/2024/11/nvidia-ai-innovations-transforming-game.html">https://newspovgamingstyle.blogspot.com/2024/11/nvidia-ai-innovations-transforming-game.html</a> <a href="https://newspovgamingstyle.blogspot.com/2024/11/amazons-ai-innovations-transforming.html">https://newspovgamingstyle.blogspot.com/2024/11/amazons-ai-innovations-transforming.html</a> <a href="https://hatebin.com/bjlfiivdea">https://hatebin.com/bjlfiivdea</a> <a href="https://hatebin.com/pskqybfrso">https://hatebin.com/pskqybfrso</a> <a href="https://hatebin.com/wonfztqbmu">https://hatebin.com/wonfztqbmu</a> <a href="https://hatebin.com/mldnwdwjaz">https://hatebin.com/mldnwdwjaz</a> <a href="https://hatebin.com/yqoavcigkc">https://hatebin.com/yqoavcigkc</a> <a href="https://hatebin.com/zgjhaujaib">https://hatebin.com/zgjhaujaib</a> <a href="https://0bin.net/paste/xik+Uy0M#Vl9eiSwLB8cHbu2CqMVynzZ+PjFagSgR4VK1y5x0KiL">https://0bin.net/paste/xik+Uy0M#Vl9eiSwLB8cHbu2CqMVynzZ+PjFagSgR4VK1y5x0KiL</a> <a href="https://0bin.net/paste/TXcyn7yw#bh4E-b5QYUBy8/M3/Rrgya/cP7uehyOFDeP3O/mrnoe">https://0bin.net/paste/TXcyn7yw#bh4E-b5QYUBy8/M3/Rrgya/cP7uehyOFDeP3O/mrnoe</a> <a href="https://0bin.net/paste/hVQxNJtP#TJdtYPZyEtxM1bt12Z50BZkDFrpwBNqNwSMnKyd549A">https://0bin.net/paste/hVQxNJtP#TJdtYPZyEtxM1bt12Z50BZkDFrpwBNqNwSMnKyd549A</a> <a href="https://0bin.net/paste/k-tafjTB#ensw59Wt+QYSWzOUZZioTFmXtTigKeAwZm9yOfjNKcv">https://0bin.net/paste/k-tafjTB#ensw59Wt+QYSWzOUZZioTFmXtTigKeAwZm9yOfjNKcv</a> <a href="https://www.pastery.net/fpjcwd/">https://www.pastery.net/fpjcwd/</a> <a href="https://www.pastery.net/bhcqba/">https://www.pastery.net/bhcqba/</a> <a href="https://www.pastery.net/udctmh/">https://www.pastery.net/udctmh/</a> <a href="https://www.pastery.net/jecxjc/">https://www.pastery.net/jecxjc/</a> <a href="https://justpaste.it/fxjje">https://justpaste.it/fxjje</a> <a href="https://briselme4brisel.hashnode.dev/amazon-ai-strategy-for-retail-personalization">https://briselme4brisel.hashnode.dev/amazon-ai-strategy-for-retail-personalization</a> <a href="https://briselme4brisely.hashnode.space/default-guide/nvidia-ai-breakthroughs-in-climate-modeling?t=1731481808722">https://briselme4brisely.hashnode.space/default-guide/nvidia-ai-breakthroughs-in-climate-modeling?t=1731481808722</a> <a href="https://medium.com/@asgarigiray/intel-ai-powered-solutions-for-5g-networks-3416ea1c12dc">https://medium.com/@asgarigiray/intel-ai-powered-solutions-for-5g-networks-3416ea1c12dc</a> <a href="https://web3devcommunity.com/topic/38935/apple-ai-in-augmented-reality-applications">https://web3devcommunity.com/topic/38935/apple-ai-in-augmented-reality-applications</a> <a href="https://web3devcommunity.com/topic/38937/frgergerererger">https://web3devcommunity.com/topic/38937/frgergerererger</a> <a href="https://sebsauvage.net/paste/?fb3859063eb2301f#n1ZarExg+YXX3303e+DLQ5STFqymX1Ou3k/9rbROJYI=">https://sebsauvage.net/paste/?fb3859063eb2301f#n1ZarExg+YXX3303e+DLQ5STFqymX1Ou3k/9rbROJYI=</a> <a href="https://thedarkko.net/topic/171552-samsung-ai-technologies-elevating-mobile-photography/">https://thedarkko.net/topic/171552-samsung-ai-technologies-elevating-mobile-photography/</a> <a href="https://pastelink.net/ishpwrlr">https://pastelink.net/ishpwrlr</a> <a href="https://pastelink.net/pwldd23d">https://pastelink.net/pwldd23d</a> <a href="https://pastelink.net/ic4dbc6i">https://pastelink.net/ic4dbc6i</a> <a href="https://pastelink.net/5t6bewxc">https://pastelink.net/5t6bewxc</a> <a href="https://pastelink.net/kiec76om">https://pastelink.net/kiec76om</a> <a href="https://pastelink.net/s1a62ppi">https://pastelink.net/s1a62ppi</a> <a href="https://paste.ee/p/Nu8hr">https://paste.ee/p/Nu8hr</a> <a href="https://paste.ee/p/xPGRE">https://paste.ee/p/xPGRE</a> <a href="https://controlc.com/b195c449">https://controlc.com/b195c449</a> <a href="https://controlc.com/ea14ad7c">https://controlc.com/ea14ad7c</a> <a href="https://controlc.com/5917093e">https://controlc.com/5917093e</a> <a href="https://controlc.com/56be7591">https://controlc.com/56be7591</a> <a href="https://controlc.com/6ce67cc9">https://controlc.com/6ce67cc9</a> <a href="https://paste.imirhil.fr/?46673c10c06b5561#ouBq4gIcBoEyYf6l73C4uRgCEZe/88zl7+KPDcsdTTM=">https://paste.imirhil.fr/?46673c10c06b5561#ouBq4gIcBoEyYf6l73C4uRgCEZe/88zl7+KPDcsdTTM=</a> <a href="https://rentry.co/whqgch2r">https://rentry.co/whqgch2r</a> <a href="http://opensource.platon.org/forum/projects/viewtopic.php?p=12692889#12692889">http://opensource.platon.org/forum/projects/viewtopic.php?p=12692889#12692889</a> <a href="https://wokwi.com/projects/414422004826960897">https://wokwi.com/projects/414422004826960897</a> <a href="https://cloudim.copiny.com/question/details/id/954783">https://cloudim.copiny.com/question/details/id/954783</a> <a href="https://matters.town/a/koxz3niaxspu">https://matters.town/a/koxz3niaxspu</a> <a href="https://paste.centos.org/view/ab529b6a">https://paste.centos.org/view/ab529b6a</a> <a href="https://ctxt.io/2/AAB4AfcAFA">https://ctxt.io/2/AAB4AfcAFA</a> <a href="https://paste.feed-the-beast.com/view/612284b6">https://paste.feed-the-beast.com/view/612284b6</a> <a href="https://jsitor.com/Qni21lCN2n">https://jsitor.com/Qni21lCN2n</a> <a href="https://jsitor.com/6btLcfCKQg">https://jsitor.com/6btLcfCKQg</a> <a href="https://etextpad.com/tf2cdetput">https://etextpad.com/tf2cdetput</a> <a href="https://yamcode.com/nvidia-ai-applications-for-autonomous-drones">https://yamcode.com/nvidia-ai-applications-for-autonomous-drones</a> <a href="https://prod.pastebin.prod.webservices.mozgcp.net/HGBouQoq">https://prod.pastebin.prod.webservices.mozgcp.net/HGBouQoq</a> <a href="https://commie.io/#2xApHgFF">https://commie.io/#2xApHgFF</a> <a href="https://privatebin.net/?b4265e3eb4f8e904#9EwSH3uD8CjTv4p79W8KRrPVCKwEtA7xfxGY4e6jCbqa">https://privatebin.net/?b4265e3eb4f8e904#9EwSH3uD8CjTv4p79W8KRrPVCKwEtA7xfxGY4e6jCbqa</a> <a href="https://paste.enginehub.org/m86vR9fyx">https://paste.enginehub.org/m86vR9fyx</a> <a href="https://binshare.net/mckLNYtVcgMv9izUWZpZ">https://binshare.net/mckLNYtVcgMv9izUWZpZ</a> <a href="https://binshare.net/nq7dprfuWfr8ktcKPvOH">https://binshare.net/nq7dprfuWfr8ktcKPvOH</a> <a href="https://binshare.net/s9Z1NezxQTXaQihcGfiM">https://binshare.net/s9Z1NezxQTXaQihcGfiM</a> <a href="https://binshare.net/NyqfrVFGQRaFvTnFAIfR">https://binshare.net/NyqfrVFGQRaFvTnFAIfR</a> <a href="https://paste.toolforge.org/view/6f8d0455">https://paste.toolforge.org/view/6f8d0455</a> <a href="https://snippet.host/axbzrf">https://snippet.host/axbzrf</a> <a href="https://wow.curseforge.com/paste/06c59902">https://wow.curseforge.com/paste/06c59902</a> <a href="https://www.wowace.com/paste/d9568726">https://www.wowace.com/paste/d9568726</a> <a href="https://www.taskade.com/d/XVbcbG8sYAMiVnK5?share=edit&edit=GZ9GKE1hhGfVQaxW&as=list">https://www.taskade.com/d/XVbcbG8sYAMiVnK5?share=edit&edit=GZ9GKE1hhGfVQaxW&as=list</a> <a href="https://forum.thecodingcolosseum.com/topic/18504/samsung-ai-use-in-voice-recognition-software">https://forum.thecodingcolosseum.com/topic/18504/samsung-ai-use-in-voice-recognition-software</a> <a href="https://etextpad.com/xyqlkwdhhx">https://etextpad.com/xyqlkwdhhx</a> <a href="https://www.calameo.com/read/007814943a78cbf4781f4">https://www.calameo.com/read/007814943a78cbf4781f4</a> <a href="https://www.deviantart.com/pickman514854/journal/NVIDIA-AI-Capabilities-in-Real-Time-3D-Rendering-1121623714">https://www.deviantart.com/pickman514854/journal/NVIDIA-AI-Capabilities-in-Real-Time-3D-Rendering-1121623714</a> <a href="https://promosimple.com/ps/2ff93/the-most-scenic-road-trips-to-take-in-2024">https://promosimple.com/ps/2ff93/the-most-scenic-road-trips-to-take-in-2024</a> <a href="https://hackmd.io/@XPvGJkTlQE6Tq9bywho50w/r1bCaa-Myl">https://hackmd.io/@XPvGJkTlQE6Tq9bywho50w/r1bCaa-Myl</a></code></pre>
Replication Package: The Past, Present, and Future of Research on the Continuous Development of AI
<p>Replication package for the publication regarding the <strong>The Past, Present, and Future of Research on the Continuous Development of AI.</strong></p> <p> </p> <p> </p>
Recommender Systems and AI Techniques in E-commerce: An Analysis of Trends and the Research Agenda
Open the record for dataset details and reuse information.
Evaluating AI-generated Research Plans - Appendix
Open the record for dataset details and reuse information.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Multicenter Study Protocol: Research on Evaluation and Detection of Surgical Wound Complications with AI-based Recognition. (REDSCAR-trial)
ClinicalTrials.gov study NCT06771726. IPD Sharing: YES. Countries: 1. Publications: 2.
The Research of AI Assistant Gastroscope Training
ClinicalTrials.gov study NCT04682821. IPD Sharing: NO. Countries: 1. Publications: 1.
The AI Prognostic Assessment and Pathological Basis Research of Early HCC After Minimally Invasive Treatment
ClinicalTrials.gov study NCT04299919. IPD Sharing: YES. Countries: 1. Publications: 21.
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
Generative AI in Education and Research: A systematic mapping and bibliometric analysis of published articles
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Video Presentation for research paper "Evaluation of Vessel CO2 Emissions Methods using AIS Trajectories" in SSTD'23
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Replication Package: Threats to Validity in Green AI Research
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Figures 8-11 from: Khalaim AI, Ruíz-Cancino E (2017) Ichneumonidae (Hymenoptera) associated with xyelid sawflies (Hymenoptera, Xyelidae) in Mexico. Journal of Hymenoptera Research 58: 17-27. https://doi.org/10.3897/jhr.58.12919
Figures 8-11 - Idiogramma elbakyanae sp. n., holotype female 8 habitus (without antennae and wings), lateral view 9 head, antero-ventral view 10 head, dorsal view 11 head, dorso-lateral view.
Figures 1-7 from: Khalaim AI, Ruíz-Cancino E (2017) Ichneumonidae (Hymenoptera) associated with xyelid sawflies (Hymenoptera, Xyelidae) in Mexico. Journal of Hymenoptera Research 58: 17-27. https://doi.org/10.3897/jhr.58.12919
Figures 1-7 - Gelanes horstmanni sp. n., holotype female (1, 2, 4, 5) and paratype male (3). 1 body, lateral view 2 head with antenna and mesosoma, dorso-lateral view 3 head, dorsal view 4 head, antero-ventral view 5 apex of ovipositor, lateral view. Idiogramma comstockii, female 6 head, antero-ventral view 7 head, dorso-lateral view.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.