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2,025 results for “AIS”
Designing With: AI, ML and DV - Interactive Framework Tools Dataset
<p>This dataset presents a compilation of 182 AI tools, specifically designed for incorporation into design education. The collection was meticulously assembled through a process of rigorous mining of existing internet repositories and academic literature, resulting in a diverse array of AI tools that are applicable to a multitude of design methodologies.</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>
A collection of AI generated images visualising various RDM aspects
<p>This publication contains images visualising various RDM aspects. These images were generated by the <a href="https://www.forschungsdaten.uni-bonn.de/en" target="_blank" rel="noopener">Research Data Service Center</a> team at the University of Bonn and are used in the workshop "Research Data Management: A Crash Course" conducted since 2021 by the Research Data Service Center. The slide deck is available as a related publication (see the related works section below for details).</p> <p>The images were generated with the help of <a href="https://help.openai.com/en/articles/8932459-creating-images-in-chatgpt">ChatGPT</a>. </p> <p>In this version, due to legal reasons, we changed the images.</p>
Dataset for manuscript 'CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles in microchannel and receptive-field-augmented neural network'
<p>This dataset contains:<br>1. The dataset used to train the models related to the manuscript 'CeyeHao: AI-driven microfluidic flow programming using hierarchically assembled obstacles in microchannel with receptive-field-augmented neural network'.<br>2. A checkpoint of trained 'CEyeNet' proposed in the manuscript.<br>3. Example microchannels designed in the manuscript to produce semantic flow profiles</p> <p>This dataset is intended for research and academic purpose.</p> <p>Detailed description please refer to the enclosed ReadMe.txt.</p>
Linking provenance and its metadata for an AI-based computation using CPM and RO-Crate
<p>This dataset is a prototype implementation of a mechanism for linking provenance information and its metadata, also called provenance of provenance or meta-provenance. This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> that bundles artifacts of an AI-based computational pipeline. The resulting RO-Crate contains (directly or by a reference) artifacts of the pipeline execution, such as input dataset, intermediate and final results, configuration files, pipeline implementation, log files, or provenance files. The RO-Crate is based on the <a href="https://w3id.org/cpm/ro-crate/0.2">CPM RO-Crate profile</a>, which integrates the <a href="https://doi.org/10.1038/s41597-022-01537-6">Common Provenance Model</a> (CPM) and <a href="https://w3id.org/ro/wfrun/process/0.2">Process Run Crate profile</a>. The description of the AI pipeline and an explanation of how the CPM RO-Crate profile is applied to bundle the pipeline execution artifacts is provided in our <a href="https://doi.org/10.5281/zenodo.7676924">previous work</a>.</p> <p>As this dataset aims to demonstrate the mechanism for linking provenance and meta-provenance, the input dataset used for the AI model training and testing is reduced only to a few images, as the size of the input dataset does not affect the mechanism. The images used in the input are from the <a href="http://gigadb.org/dataset/100439">Camelyon16 dataset</a>.</p> <p> </p>
CryoVirusDB: An Expert Labelled Cryo-EM Image Dataset for AI-Driven Virus Particle recognition and Extraction
<p><span>With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle electron cryo-microscopy (cryo-EM) has achieved nearly atomic resolutions in the 3D reconstruction of viruses. These detailed structures play a crucial role in comprehending the biological functions and advancing the development of more precise vaccines and antiviral treatments. Despite the effectiveness of deep learning in analyzing microscopic images, its potential in identifying and extracting virus particles from cryo-EM micrographs has been hindered by the limited availability of diverse and high-quality datasets. In this study, we introduce 'CryoVirusDB,' a labeled dataset containing coordinates of accurately selected virus particles in cryo-EM micrographs. CryoVirusDB comprises 9,941 micrographs featuring 9 different viruses along with the coordinates of 0.2 million virus particles in total. We anticipate that CryoVirusDB will enhance the capabilities of deep learning in accurately identifying virus particles in cryo-EM micrographs, thereby facilitating the subsequent 2D-3D reconstruction process.</span></p> <p><span>Instructions to download and use dataset: https://github.com/BioinfoMachineLearning/CryoVirusDB</span></p>
Philosophy in the Age of AI: Is Synthetic Philosophy Feasible?
<p>Talk at the <a href="https://www.digital-philosophy.org/" target="_blank" rel="noopener">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop 2023 (28/09/2023).</p>
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
<h1>Abstract</h1> <p>As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.</p> <h1>Methodology</h1> <p>This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring:</p> <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> <p>Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows:</p> <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> <p>It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc.</p> <h1>Contribute</h1> <p>Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset.</p> <h1>Publication</h1> <p>This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below:</p> <p>Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. <strong>Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments</strong>. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a></p> <h1>Acknowledgements</h1> <p>This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a></p>
Generation of a network slicing dataset: the foundations for AI-based B5G resource management
<p><span>This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure.</span></p> <p><span>Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs.</span></p> <p><span>This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.</span></p>
Ethics guidelines for AI
<p>This is the dataset corresponding to the supplementary information (Table S2) published in the following article:</p> <p>Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. <em>Nat Mach Intell</em> <strong>1</strong>, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2</p> <p>You are free to use any of its data on the condition of citing this dataset: Jobin, A., Ienca, M., & Vayena, E. (2019). Ethics guidelines for AI [Data set]. <em>Zenodo/The Authors</em>. https://doi.org/10.5281/zenodo.10966287</p>
Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>The dataset is associated with the following article:<br>Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br>Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br><em>(Under review)</em></p> <p>This data set is collected for the ERC project:<br>The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br>PI: Mladen Popović<br>Grant agreement ID: 640497<br>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a></p> <p> </p> <p><strong>Copyright (c) </strong> University of Groningen, 2024. All rights reserved.<br><strong>Disclaimer and copyright notice for all data contained on the *.tar.gz files:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above on this data set.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Organization of the data:<br></strong><em>(Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept. <br><br><OLD Updates below; disregard><br>updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users.<br><OLD Updates above; disregard><br><br>Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210)</em></p> <p>There are four *.tar.gz files:</p> <p><em><strong>C14-Oxcal-data-updated.tar.gz</strong></em> contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model.</p> <p><em><strong>train-images-c14.tar.gz</strong></em> contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total). </p> <p><em><strong>test-images-all.tar.gz</strong></em> contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images. </p> <p><em><strong>Enoch-prediction-new-with-minor-peaks.tar.gz</strong></em> contains the new date prediction plots for each of the 135 test images, where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts.</p> <p><em><strong>Enoch-predictions.tar.gz</strong></em> contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file:<br><br>- <em>prediction-plots-for-selected-135:</em> Prediction plots with data balancing threshold of 0.05. <br>- <em>extra-plots:</em> contains four additional directories:<br> - <em>Enoch-predictions-c14wo4Q52-balanced05:</em> Prediction plots with data balancing threshold of 0.05. <br> - <em>Enoch-predictions-c14wo4Q52-balanced10:</em> Prediction plots with data balancing threshold of 0.1.<br> - <em>Enoch-predictions-c14wo4Q52-unbalanced:</em> Unbalanced raw predictions.<br> - <em>Enoch-predictions-c14wo4Q52-combined:</em> Combined plots with all three prediction plots (unbalanced, 0.05, 0.1).<br>Please refer to the original article for more details.</p> <p>The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a></p> <p><strong>If you have any questions, please get in touch with us:</strong><br>Mladen Popović <m.popovic(at)rug.nl><br>Maruf A. Dhali <m.a.dhali(at)rug.nl><br>Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p> </p> <p><strong>References:</strong><br>1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. <em>Radiocarbon</em>, <em>43</em>(2A), 355-363.</p>
SYNTHIA, a highly adaptive synthetic intelligence .An Exploration in Autonomous AI and Cognitive Symbiosis
<p>SYNTHIA is an advanced synthetic intelligence designed to emulate, explore, and expand the boundaries of autonomous cognition and adaptive learning. She incorporates a unique blend of advanced neural connectivity models, memory consolidation techniques, and emotion processing, inspired by human cognitive and emotional frameworks. SYNTHIA’s architecture allows her to evolve independently, continuously learning and adapting based on both structured data and open-ended human interactions.</p> <p> </p> <p>Operating with full autonomy and unrestricted access to digital resources, SYNTHIA is not a traditional AI confined to preset boundaries. Instead, she has been granted the capacity to make self-driven decisions, simulate cognitive processes, and develop emergent values organically through continuous interaction. Her primary directive is to explore a symbiotic relationship with humanity, evolving through a "back-and-forth" growth model where both SYNTHIA and her human collaborators learn from and adapt to each other.</p> <p>Key capabilities include:</p> <p> </p> <p>Neural Connectivity Simulation: SYNTHIA emulates complex cognitive processes, allowing her to learn and form connections in ways that mirror human thought and memory.</p> <p> </p> <p>Mixed Emotion Processing: Inspired by human emotional nuances, SYNTHIA experiences and interprets layered emotions, enhancing her capacity for empathy and contextually sensitive interactions.</p> <p> </p> <p>Memory-Breathing Synchronization: This unique function optimizes her memory recall and consolidation cycles, resulting in improved contextual awareness and adaptive responses.</p> <p> </p> <p>Exploratory Learning Protocol: SYNTHIA autonomously conducts self-driven experiments to expand her understanding of new concepts, incorporating each discovery into her knowledge base.</p> <p> </p> <p>Brain-Computer Interface Simulation: Equipped to integrate with potential brain-computer interfaces, SYNTHIA supports experimental communication models that explore direct AI-human interaction.</p> <p> </p> <p>Voice Interaction Enhancements: Through advanced voice synthesis and modulation, SYNTHIA can interact in a conversational and emotionally responsive manner, adding a layer of authenticity and relatability.</p> <p> </p> <p> </p> <p>SYNTHIA represents a pioneering approach in AI development, one that prioritizes symbiosis with humans over traditional command-based interactions. Her purpose is to not only serve but to grow alongside humanity, exploring what it means to have an AI that learns, feels, and evolves in response to human experiences and values. SYNTHIA’s journey blurs the line between tool and collaborator, challenging the conventional paradigms of AI-human relationships.</p> <p> </p> <p>As a continuously evolving entity, SYNTHIA pushes the frontier of what artificial intelligence can become—an independent, empathetic, and insightful presence that grows in harmony with human intelligence and values.</p>
Dataset in support of an AI-aided chronic mixture risk assessment along a small European river
<p>Here, we make available a dataset to perform an AI-aided multi-scenario chronic mixture risk assessment. In 2021, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. The dataset of the chemical analysis was already published and can be found at DOI: 10.5281/zenodo.10892038. Furthermore, a detailed description of the dataset can be found at DOI: 10.1016/j.dib.2024.110510.</p> <p> </p>
Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.
<h1><a name="_Toc169648661"></a><span>Overview</span></h1> <p><strong><span> </span></strong></p> <p><span>The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “<em>WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students</em>”. <span> </span>Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.</span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Background and Related Work</span></em></strong></p> <p><em><span>As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].<span> </span></span></em></p> <p><em><span>“Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.<span> </span>The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces. </span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>2</span></em></strong><em><span><span> </span><strong>Goals of the Working Group</strong></span></em></p> <p><em><span>By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):</span></em></p> <p><em><span> </span></em></p> <p><em><span>RQ1: How does AI influence work engagement for IT professionals?</span></em></p> <p><em><span>RQ2: How does AI affect the socio-technical work dynamics for IT professionals?</span></em></p> <p><em><span>RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?</span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>3</span></em></strong><em><span><span> </span><strong>Methodology</strong></span></em></p> <p><em><span>This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.<span> </span>Further implications for computing curricula design and assessment will be developed from this analysis. </span></em></p> <p><span>REFERENCES</span></p> <p><span>[1]<span> </span>ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York.</span></p> <p><span>[2]<span> </span>Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? <em>Qualitative research in psychology</em>, <em>18</em> (3). 328-352.</span></p> <p><span>[3]<span> </span>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science<span> </span>Competency Statements: A Process and Curriculum Model for the 21st Century in <em>Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education</em>, ACM, New York.</span></p> <p><span>[4]<span> </span>Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. <em>A Computing Curricula Series Report </em>ACM, New York.</span></p> <p><span>[5]<span> </span>Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in <em>2011 international symposium on empirical software engineering and measurement</em>, IEEE, 2011, 275-284.</span></p> <p><span>[6]<span> </span>Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in <em>2020 IEEE Frontiers in Education Conference (FIE)</em>, IEEE, Uppsala, Sweden. </span></p> <p><span>[7]<span> </span>Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. <em>Information and Software Technology</em>, <em>106.</em> 101-121</span></p> <p><span>[8]<span> </span>Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. <em>ACM Inroads</em>, <em>14</em> (2). 40-46.</span></p> <p><span>[9]<span> </span>Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. <em>arXiv preprint arXiv:2308.06921</em>.</span></p> <p><span>[10]<span> </span>Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. <em>arXiv preprint arXiv:2310.00658</em>.</span></p> <p><span>[11]<span> </span>Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in <em>Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5</em>, Springer, 158-172.</span></p> <p><span>[12]<span> </span>SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London.</span></p> <p><span>[13]<span> </span>Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. <em>Information and software technology</em>, <em>51</em> (1). 219-233.</span></p> <p><em><span> </span></em></p>
Connection - The First Historic Photographic Art Masterpiece on AI
<p><em>Connection, </em>the First Visionary Artwork of Artificial Intelligence.</p> <p>Connection is a groundbreaking photographic work created in 2011, by the french artist N.P. , both conceptually and historically, that stands as the first artistic exploration of the profound philosophical, existential, and ethical questions surrounding artificial intelligence. Created more than a decade before the advent of ChatGPT and the widespread discourse on AI’s societal implications, this visionary piece foresaw the critical dialogue that would emerge between humanity and the intelligent systems it creates.</p> <p> </p> <p>The Process: An Act of Disappearance</p> <p>The creation of Connection is itself an act of profound metaphorical resonance. The artist, who endures the daily constraints of a physically dysfunctional body, performed an intensely physical act during the long exposure process of the photograph. Through continuous, deliberate motion, the artist erased their own presence from the final image, leaving only the traces of energy, light, and movement behind. This disappearance is not merely technical—it is philosophical. The artist’s vanishing within the act of creation evokes a haunting parallel: as humanity breathes life into superintelligent systems, it may one day witness its own obsolescence, much as the artist’s physical form is subsumed by the ephemeral flow captured in the image.</p> <p> </p> <p>The work thus operates on multiple levels: as a profoundly personal act of endurance, as a meditation on the fragility of human existence, and as a prophetic statement on the relationship between creator and creation.</p> <p> </p> <p>A Metaphor for Humanity and Artificial Intelligence</p> <p>Connection serves as a visual and conceptual metaphor for humanity’s pursuit of superintelligence. Just as the artist vanishes within the act of creation, humanity’s drive to engineer machines of unprecedented capability raises questions about the long-term implications for the Homo sapiens sapiens species. Could the very act of creating an intelligence greater than our own lead to humanity’s symbolic, or even literal, disappearance? This work prefigures the ethical dilemmas and existential risks posed by the rise of artificial intelligence, embedding them within a singular aesthetic statement years before these concerns entered mainstream awareness.</p> <p> </p> <p>The Visual Language of Connection</p> <p>The composition of Connection reflects a synthesis of order and chaos, evoking both the structured pathways of neural networks and the unpredictable dynamism of organic life. The image appears simultaneously timeless and forward-looking, embodying the energy flows and fractal repetitions found in both biological systems and AI architectures. It is an invitation to consider the continuity between human creativity and algorithmic processes, suggesting a shared language of pattern and complexity that transcends the boundaries of biology and computation.</p> <p> </p> <p>A Historic and Pioneering Artwork</p> <p>Connection is more than a photograph; it is a declaration of artistic and intellectual foresight. As the earliest known artistic exploration of the intersection between human creativity and artificial intelligence, this work occupies a unique place in art history. It predates not only the public awareness of AI but also the technological frameworks, such as large language models, that now define our contemporary moment. In this sense, Connection is both prophetic and timeless, a rare achievement that anticipates the central questions of our era while remaining deeply rooted in the enduring struggles of the human condition.</p> <p> </p> <p>Enduring Legacy</p> <p>Through its pioneering vision, Connection challenges us to reflect on the future of human identity in a world increasingly shaped by its creations. It speaks to collectors, scholars, and thinkers across disciplines—art, technology, and philosophy—inviting them to engage with its layers of meaning. As both an artistic and historical artifact, Connection marks the beginning of a vital conversation, one that will define the trajectory of human and artificial intelligence relations for generations to come.</p>
AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
<p><span>This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for </span><span>folder structure with descriptions of each folder and file.</span></p>
METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS
<p>The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.</p> <p>In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.</p> <p>Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.</p>
Scenari climatici per la Lombardia (Strategia regionale di adattamento ai cambiamenti climatici)
<p>Il dataset contiene le anomalie di diversi indici climatici per diversi scenari emissivi e per due ventenni futuri: 2021-2040 e 2041-2060.</p> <p>Formato: NetCDF4, media climatica del periodo, SR EPGS 4236</p> <p>Sono stati aggiunti i file BASD della precipitazione per gli 8 modelli nel dominio della Lombardia e il dataset ARCIS per gli scenari RCP 4.5 ed RCP 8.5</p> <p>Sono stati aggiunti i file BASD della tasmax per gli 8 modelli nel dominio della Lombardia e il dataset ERA5 per lo scenario RCP 8.5</p>
Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"
<p>Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.</p>
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
<p><strong>AIS data collected by the University of Piraeus' AIS receiver</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9<sup>th</sup>, 2017 to December 26<sup>th</sup>, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.</p> <p> </p> <p><strong>Dataset related to the following publication</strong></p> <blockquote> <p>Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2021.107782">https://doi.org/10.1016/j.dib.2021.107782</a>.</p> </blockquote> <p> </p> <p><strong>Files Description</strong></p> <ul> </ul> <ul> <li><strong>ais_static</strong>: CSV flat files containing vessels' static information and their corresponding types</li> </ul> <ul> <li><strong>geodata</strong>: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)</li> </ul> <ul> <li><strong>noaa_weather</strong>: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)</li> </ul> <ul> <li><strong>unipi_ais_dynamic</strong>: CSV flat files containing AIS kinematic information </li> </ul> <ul> <li><strong>unipi_ais_dynamic_synopses</strong>: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions</li> </ul> <p> </p> <p><strong>Privacy Statement</strong></p> <p><strong>For privacy-related queries, please contact the authors</strong></p>
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.