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2,025 results for “AIS”
FOSS4G-IT 2021 - Workshop "Introduzione ai database geospaziali" (a cura di Enrico Ferreguti, Luca Lanteri e Rocco Pispico)
<p><strong>Workshop "Introduzione ai database geospaziali" a cura di Enrico Ferreguti, Luca Lanteri e Rocco Pispico</strong></p> <p>20 settembre 2021</p> <p>Usare le estensioni geospaziali del linguaggio SQL per condividere. analizzare, riassumere ed aggiornare i nostri dati geografici. Per seguire il workshop è sufficiente QGIS LTR e un browser.</p>
Experimental Data for the paper Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians
<p>This upload contains supplementary materials for the paper <br> Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians, by Abdullah Khan, Alexei Lisitsa and Alexei Vernitski, to appear in Proceedings of ICAART 2022 (14th International Conference on Agents and Artificial Intelligence, 3-5 February, 2022), SCITEPRESS. </p>
eDoer - A Human-AI based Learning Environment - Ethics and Privacy related issues
<p>The eDoer platform was presented on the first Ethical, Legal, and Societal Aspects (ELSA) workshop of the German NFDI FAIR Data Spaces community. </p> <p>eDoer platform: <a href="http://edoer.eu/%C2%A0">http://edoer.eu/ </a></p>
An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode
<p><strong>Abstract</strong><br> AlphaCode, the code generation system by DeepMind, is an AI technology for assisting software developers in solving competitive programming problems using natural language problem descriptions.<br> However, there is no existing work comparing AlphaCode-generated codes with human codes.<br> In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes.<br> We collect 44 generated codes in C++ and Python languages from AlphaCode official website that solve 22 problems on Codeforces.<br> We then retrieve 31,736 human codes by using Codeforces API.<br> The results show that (i) the generated code from AlphaCode and the human code is not similar (i.e., the similarity of 0.26 for both C++ and Python), however, the code fragments in the generated code are comprised of various human codes (i.e., uniqueness of 3.30\% and 8.94\% for C++ and Python respectively) and<br> (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage.<br> Moreover, AlphaCode employs excessive nested loops and unnecessary variable declarations (e.g., used \textit{long long} instead of \textit{int}), which causes a low performance regarding our manual investigation.</p>
Secondo episodio di interviste ai premiati del Premio Nazionale di Filosofia, edizione 2022. Seconda parte, 04/08/2022.
<p>Interviste ai premiati del Premio Nazionale di Filosofia, edizione 2022. Seconda parte, 04/08/2022.</p> <p>Professori, ricercatori e studiosi intervistano i premiati del Premio Nazionale di filosofia che hanno risposto affermativamente alla richiesta di essere intervistati.</p> <p>Poi premiati e intervistatori (con i premiati che da intervistati diventano intervistatori) intervistano la scrittrice Patrizia Palombi.</p>
Episodio di interviste ai premiati del Premio Nazionale di Filosofia, edizione 2022. Prima parte, 26/07/2022
<p>Interviste ai premiati del Premio Nazionale di Filosofia, edizione 2022. Prima parte, 26/07/2022.</p> <p>Professori, ricercatori e studiosi intervistano i premiati del Premio Nazionale di filosofia che hanno risposto affermativamente alla richiesta di essere intervistati.</p> <p>Poi premiati e intervistatori (con i premiati che da intervistati diventano intervistatori) intervistano la scrittrice Maria Elisa Gualandris.</p>
Terzo episodio di interviste ai premiati del Premio Nazionale di Filosofia, edizione 2022
<p>Terzo episodio di interviste ai premiati (che hanno risposto affermativamente alla proposta di intervista) del Premio Nazionale di Filosofia, edizione 2022</p>
Dataset for the Job/Site AI matching problem in GlideinWMS
<p>Dataset used for the time-series analysis and classification analysis.</p>
Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI Dataset
Open the record for dataset details and reuse information.
AI-BASED INSPECTION OF RECYCLED CARBON FIBRE FABRIC
<p>The dataset is part of an EU-funded project and is included in the paper entitled "AI-based Inspection of Recycled Carbon Fibre Fabric."</p> <p>The work presented in this paper is related to the project “MC4” and has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No 101057394. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. </p> <p> </p> <p><a href="../api/records/11203952/draft/files/sample_images.zip/content" target="_blank" rel="noopener noreferrer">sample_images.zip</a> - It is a zip file containg the .png images. These are the samples images of the output from the in-house developed sensor.</p> <p><a href="../api/records/11203952/draft/files/dataset_samples.pkl/content" target="_blank" rel="noopener noreferrer">dataset_samples.pkl</a> - It is a pickle file. This contains the dataset whose results are reported in the paper .</p> <p><a href="../api/records/11203952/draft/files/load_data.py/content" target="_blank" rel="noopener noreferrer">load_data.py</a> - This is the sample code to load the data. Explanation to the shapes and the structure of the data are present here.</p>
A Concept for Integrating AI-based Support Systems into Clinical Practice
<p>This repository refers to a contribution entitled "<strong>A Concept for Integrating AI-based Support Systems into Clinical Practice</strong>" submitted to the <strong>34th Medical Informatics Europe Conference</strong>.</p>
Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts
<p>In this repository are all the data/scripts necessary to reproduce the results of the article 'Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts.'</p> <p>The files in NetCDF format and nomenclature <strong>TYPE</strong>_<strong>FORECAST</strong>_202201_202312.nc are the input data for the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain all the predictions from the atmospheric models and the 'observations.' This input data was divided into training, validation, and test periods.</p> <p>The files in NetCDF format and nomenclature <strong>CNN</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the output data of the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain the TMAX and TMIN forecasts for the test period.</p> <p>The files in NetCDF format and nomenclature <strong>MODEL</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the forecasts from the atmospheric models (BRAMS, ETA, WRF, SMEC) and the observations (SAMET) for the same test period. This data was used to compare the predictions of each model with the predictions of the neural network.</p> <p>The file CCN_Unet.py is a Python program containing the configurations of the convolutional neural network with a U-Net architecture used in the study.</p> <p>The files in ASCII format (.py, .gs) are scripts used for visualizing the results and generating the graphs. The other files are shapefiles and masks used for generating figures.</p>
Dataset Analisis Dampak AI Terhadap Mahasiswa SI ITS
<p>Dataset yang digunakan pada paper Analisis Pengaruh Artificial Intelligence (AI) Terhadap Kinerja Akademik Mahasiswa Sistem Informasi ITS</p>
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>
AI concepts relevant for AI and e-commerce/retail secors
<p><strong>INAIR</strong> (<a href="https://www.ai4retail.eu/en/">Increasing the Uptake of AI in Retail</a>) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. </p> <p>These datasets contain AI concepts identified from scientific literature as part of the INAIR Horizon Europe project. It includes keywords for skills and knowledge relevant to AI in the retail and e-commerce sectors. The data supports qualitative and quantitative analyses by providing detailed references to articles discussing each concept. This resource aims to facilitate further research and understanding of the AI-related skills and technologies impacting the retail and e-commerce industries.</p> <p>In our data retrieval process, we used the Scopus API to systematically extract the relevant publications. We collected a diverse range of publications from the database, including conference proceedings, book chapters, and other materials. To ensure the reliability of our analysis, we refined our focus exclusively to peer-reviewed articles, specifically limiting our scope to the categories of articles and reviews. </p> <p>The keywords combinations we used were: </p> <p>TITLE-ABS-KEY ( X AND Y ) AND PUBYEAR > 2017 AND PUBYEAR < 2025</p> <p>Where: X=['sales', 'retail', 'e-commerce'], Y=['AI', 'artificial intelligence']. These keywords were scrutinized within titles, abstracts, author keywords, and ‘topics’ as delineated by the platform.</p> <ul> <li>`concepts_unique.xlsx` lists unique concepts</li> <li>`concepts_master.xlsx` is a master file referencing all articles mentioning each given concept.</li> </ul> <p>The former file allows for browsing skills and knowledge concepts, while the latter serves as a reference to works addressing each concept and enables quantitative analysis, such as identifying the most frequently mentioned concepts.</p> <p> </p>
Interviste ai Premiati del Premio Nazionale di Filosofia 2024, 5 giugno 2024
<p>Secondo giorno di interviste ai vincitori del premio nazionale di filosofia, edizione 2024, e intervista dei vincitori all'ospite esterna Francesca Innocenzi</p>
Generative AI in University Communication, 2nd Wave - Survey Data (May 2024)
<p>Der Datensatz mit dem Titel "Generative KI in der Hochschulkommunikation, 2. Welle - Umfragedaten (Mai 2024)" erfasst Informationen zur Einführung und Nutzung von generativer künstlicher Intelligenz (KI) im Kontext der Hochschulkommunikation. Die Umfrage, die im Mai 2024 unter 318 deutschen Hochschulen durchgeführt wurde, von denen 82 geantwortet haben, untersucht verschiedene Aspekte, darunter Bekanntheit und Wissen über verschiedene KI-Tools (z.B. ChatGPT), Diskussionen in Gremien, das Vorhandensein von Richtlinien für die Nutzung, das Vorhandensein von Arbeitsgruppen für generative KI, strategische Ziele und Initiativen, Schulungsangebote für generative KI-Tools und die wahrgenommene Bedeutung von generativen KI-Tools in der Hochschulkommunikation. Ziel des Datensatzes ist es, Einblicke in die aktuelle Landschaft und Praxis der Integration generativer KI im universitären Umfeld zu geben. Die Daten der ersten Erhebung sind unter https://doi.org/10.5281/zenodo.10254904 zu finden.</p> <p>The dataset, titled "Generative AI in University Communication - Survey Data (May 2024)," captures information related to the adoption and utilization of generative artificial intelligence (AI) in the context of university communication. This survey, conducted in June 2024 among 318 German universities of which 82 responded, explores various aspects, including awarenes and knowledge of various AI tools (e.g. ChatGPT), discussions in committees, the existence of guidelines for usage, the presence of working groups for generative AI, strategic goals and initiatives, training offerings for generative AI tools, and the perceived importance of generative AI tools in university communication. The dataset aims to provide insights into the current landscape and practices regarding the integration of generative AI within university settings. Data of the first wave can be found here: https://doi.org/10.5281/zenodo.10254904<br><br>More information here: <a href="https://www.hof.uni-halle.de/projekte/hochki/">https://www.hof.uni-halle.de/projekte/hochki/</a></p>
AI as a driver of quality education in public universities. A bibliometric review
<p><span>Artificial intelligence (AI) is transforming teaching in public universities, enabling improvements and aiming for quality education. The purpose of this bibliometric research was to analyse the impact of AI on educational quality in public universities. The methodology used was a qualitative approach at a descriptive level, which included terms related to artificial intelligence and quality education in public universities; analysing 488 Scopus documents between 1987 and 2024, through the Vosviewer and Bibliometrix tools; and a review of 43 authors cited between 2020 and 2024 in the introduction and literature. The results reveal a growing interest in AI to personalise learning, provide high-quality resources and promote sustainability. Its potential to differentiate teaching and address specific needs of diverse learners is highlighted. It is concluded that AI can improve educational quality and equity, but strategies for ethical and responsible implementation are required, addressing challenges such as teacher training and change management.</span></p>
Replication Package: Navigating the Complexity of Generative AI Adoption in Software Engineering
<p>This paper explores the adoption of Generative Artificial Intelligence (AI) tools and Large Language Models (LLMs) within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a structured interview study with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a preliminary theoretical model of AI adoption in software: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software professionals. Our research unveils the complex dynamics at play in AI adoption within software engineering. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.</p>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
ScienceDex guides
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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.