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1,866 results for “intelligence”
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
REFLECTION CHARACTERISTICS OF RECONFIGURABLE INTELLIGENT SURFACES
<p>This dataset consists of 2 CSV files containing measurement results of the RIS reflectivity characteristics and 1 JSON file.</p> <p>The file <strong>24_04_ch_ka_3D_5_5Ghz_1_5m.csv</strong> consists of 5 columns of data. The first column contains the elevation angle, the second column contains the azimuth angle. The third column provides the number of the selected pattern – descriptions of the used patterns can be found in the file <strong>RIS_patterns_description.json</strong>. The fourth column contains the frequency at which the measurement was conducted, in Hertz.The fifth column contains the received power level measured at the receiving antenna in dBm.</p> <p> The structure of the file <strong>2D_2RIS_1_5m_16_06.csv</strong> is very similar but does not include the first column with the elevation angle.</p>
APPLICATION OF REDUCED CODEBOOK FOR RECONFIGURABLE INTELLIGENT SURFACES
<p>1 - "Best_worst_pattern.csv" - the file has four columns: index; pattern name; signal transmission frequency; received power. The file reflects the first chart in the article, which presents ordered results in ascending order from the "worst received power result" to the "best received power result."</p> <p>2 - "Scenariusz_A.csv" - the file contains the dataset used to plot the second chart, which shows the signal detection performed for Scenario A as described in the article. The file includes results for noise, Thicker vertical strips [11110000] and Right side on, each preceded by the measurement number, date, and time of the task performed.</p> <p>3 - "Scenariusz_B.csv" - the file contains the dataset used to plot the third chart, which shows the signal detection performed for Scenario B as described in the article. Similar to the previous file, each noise measurement and the next two patterns (the same as those in point 2, but labeled with numbers) are preceded by the date.</p>
Global Artificial Intelligence in Banking Market 2024–2033
<p><a href="https://www.custommarketinsights.com/report/artificial-intelligence-in-banking-market/" target="_blank" rel="noopener">Artificial Intelligence in Banking Market Size</a>, Trends and Insights By Component (Service, Solution), By Application (Fraud Detection and Prevention, Transaction Monitoring, Identity Verification, Customer Service, Virtual Assistants, Automated Customer Support, Risk Management, Credit Scoring, Market Risk Analysis, Personalized Banking, Customer Recommendations, Targeted Marketing, Compliance and Regulatory Reporting, Anti-Money Laundering (AML), Know Your Customer (KYC), Others), By Technology (Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Natural Language Processing (NLP), Text Analysis, Speech Recognition, Chatbots and Virtual Assistants, Robotic Process Automation (RPA), Process Automation, Workflow Automation, Predictive Analytics, Risk Management, Customer Insights), By Enterprise Size (Large Enterprise, SMEs), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>VC investments in AI by country</strong></p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>VC Investment</strong></td> </tr> <tr> <td><strong>US</strong></td> <td><strong>54,836</strong></td> </tr> <tr> <td><strong>China</strong></td> <td><strong>18,270</strong></td> </tr> <tr> <td><strong>EU</strong></td> <td><strong>7,921</strong></td> </tr> </tbody> </table> <p>For more details <strong>DOWNLOAD FREE SAMPLE</strong> Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189</a></p>
A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster
<p>Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.</p> <p>Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)</p> <p>RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing.</p> <p>Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv</p> <p>uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy</p>
Initial Evaluation Data for SimIMA: A Virtual Simulink Intelligent Modeling Assistant
<p>The following is our initial dataset and evaluation materials corresponding to our development and evaluation of the <a href="https://zenodo.org/record/5123570">Simulink Intelligent Modeling Assistant (SimIMA)</a>. </p> <p>We evaluate SimGestion and SimXample separately. </p> <ul> <li>The directory SimGestion-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimGestion. </li> <li>The directory SimXample-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimXample. </li> </ul> <p>This is v1.0, which is the evaluation associated with the thesis "INTELLIGENT SIMULINK MODELING ASSISTANCE VIA MODEL CLONES AND MACHINE LEARNING" by Bhisma Adhikari @ Miami University , 2021. </p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Green Intelligence Awareness, inhabitants attended in Valladolid city
<p>Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population.</p> <p>There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness. </p>
Green Intelligence Awareness, educational actions in Valladolid city
<p>Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population.</p> <p>There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness. </p>
Data and scripts for collective intelligence research (arXiv:2204.13424)
<p>This is the data and scripts for the study <strong>From Prediction Markets to Interpretable Collective Intelligence</strong> by Alexey V. Osipov and Nikolay N. Osipov (<a href="http://doi.org/10.48550/arXiv.2204.13424">arXiv:2204.13424</a> [cs.GT])</p>
TalentCLEF 2025 corpus: Skill and Job Title Intelligence for Human Capital Management
<blockquote> <p><strong>🚨 Current Status: Submission of Working Notes</strong></p> </blockquote> <p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <pre><code>@inproceedings{gasco2025overview, title={{Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management}}, author={Gasco, Luis and Fabregat, Hermenegildo and Garc\'{\i}a-Sardi{\~n}a, Laura and Estrella, Paula and Deniz, Daniel and Rodrigo, \'{A}lvaro and Zbib, Rabih}, booktitle={{International Conference of the Cross-Language Evaluation Forum for European Languages}}, year={2025}, publisher={Springer} }</code></pre> <p> </p> <h2><strong><a href="https://talentclef.github.io/talentclef/" target="_blank" rel="noopener">TalentCLEF2025</a> corpus - </strong>Task B Test set release</h2> <h3><strong>Introduction:</strong></h3> <p>The first edition of TalentCLEF aims to develop and evaluate models designed to facilitate three essential tasks:</p> <ol> <li>Finding/ranking candidates for job positions based on their experience and professional skills.</li> <li>Implementing upskilling and reskilling strategies that promote the coninuous development of workers</li> <li>Detecting emerging skills and skills gaps of importance in organizations.</li> </ol> <p>With that aim, the task is divided into two tasks: </p> <ul> <li><strong>Task A - Multilingual Job Title Matching</strong>. This task involves developing systems to identify and rank the job titles most similar to a given one by generating a ranked list of similar titles from a specified knowledge base for each job title in a provided test set.</li> <li><strong>Task B - Job Title-Based Skill Prediction. </strong>Task B requires developing systems that can retrieve relevant skills associated with a specified job title.</li> </ul> <div> <p>This data repository contains the data for these two tasks. The data is being released progressively according to the<a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/schedule/"> task schedule</a>.</p> <p>The task evaluation takes place on Codabench (<a href="https://www.codabench.org/competitions/5842/">Task A</a> and <a href="https://www.codabench.org/competitions/7059/">Task B</a>). Participants must register for the competition through <a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a> to be part of the evaluation campaign.</p> <p> </p> </div> <h3><strong>File structure: </strong></h3> <div> <div> <div> <div> <blockquote> <p>For a detailed description of the data structure, you can refer to the <a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/description_corpus/">TalentCLEF2025 data description page,</a> where it is thoroughly explained.</p> </blockquote> </div> </div> </div> </div> <p>The files is organized into two <code>*.zip</code> files, <code>TaskA.zip</code> and <code>TaskB.zip</code>, each containing training, validation and test folders to support different stages of model development. So far, only the training set for both tasks has been released, but in future releases, as the tasks progress, additional data will be added to the different subfolders for each task.</p> <p><strong>TaskA</strong> includes language-specific subfolders within the training and validation directories, covering English, Spanish, German, and Chinese job title data. The training folders for TaskA contain language-specific .tsv files for each respective language. Validation folders include three essential files—queries, corpus_elements, and q_rels—for evaluating model relevance to search queries. TaskA’s test folder has queries and corpus_elements files for testing retrieval.<br><br></p> <pre><code>TaskA/ │ ├── training/ │ ├── english/ │ │ └── taskA_training_en.tsv │ ├── spanish/ │ │ └── taskA_training_es.tsv │ └── german/ │ └── taskA_training_de.tsv │ ├── validation/ │ ├── english/ │ │ ├── queries │ │ ├── corpus_elements │ │ └── qrels │ ├── spanish/ │ ├── german/ │ └── chinese/ │ └── test/ ├── english/ │ ├── queries │ └── corpus_elements ├── spanish/ ├── german/ └── chinese/ </code></pre> <p><strong>TaskB</strong> follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published.</p> <pre><code>TaskB/ │ ├── training/ │ ├── job2skill.tsv │ ├── jobid2terms.json │ └── skillid2terms.json<br>│ ├── validation/ │ ├── queries │ ├── corpus_elements │ └── qrels │ └── test/ ├── queries └── corpus_elements </code></pre> <p><strong>Tutorials:</strong></p> <table> <tbody> <tr> <td><strong>Notebook</strong></td> <td>Link</td> </tr> <tr> <td>Data Download and Load using Python </td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_data_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Development set Baseline generation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskA_DevSet_Baseline.ipynb">Link to Colab</a></td> </tr> <tr> <td>Task B - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskB_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> </tbody> </table> <p><strong>Resources: </strong></p> <ul> <li><a href="https://talentclef.github.io/talentclef/docs/" target="_blank" rel="noopener">Web</a></li> <li><a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a></li> <li><a href="https://www.codabench.org/competitions/5842/">Codabench Task A</a></li> <li><a href="https://www.codabench.org/competitions/7059/">Codabench Task B</a></li> <li><a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/additional_resources/">Additional Resources</a></li> </ul>
Generative artificial intelligence predicts human performance
<p>Research data for a study that used generative artificial intelligence (i.e., ChatGPT with the GPT-4 and the Google Gemini 2.0 Flash models) to predict human performance in a language-based memory task. In particular, we studied the effects of context on the relatedness and memorability of garden-path sentences. </p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
Canada's national artificial intelligence governance system: Dataset from interviews with 20 government leaders & subject matter experts
<p><strong>Summary</strong></p> <p>Anonymized aggregate data from interviews with 20 government leaders and subject matter experts. The data was collected as part of a study of Canada's national system of artificial intelligence governance. The data was collected from February 2023 to July 2023. The dataset contains 610 topics that emerged from thematic analysis of interview transcripts from July 2023 to October 2023. The contexts, actors, resources, networks, evaluations, logics, functional bounds, rules, ecosystem-level dynamics, opportunities for improvement, and other topics contained in the dataset collectively represent the most significant components of Canada's national AI governance system that emerged over the course of the interviews with the 20 participants.</p> <p> </p> <p><strong>Notes for interpreting this dataset</strong></p> <p>Topics in analytical dimensions 1, 3, and 6-11 contain counts of the frequency with which aggregate topics emerged across each of the interviews with the 20 participants. Topics in analytical dimensions 2, 4, and 5 contain categories instead of frequency counts: the topics in these dimensions represent every unique actor, resource, and network that emerged over the course of the interviews instead of aggregate topics. </p> <p>Column titles contain the following abbreviations:<br>LEAD: Interviews with leaders of public sector AI governance initiatives.<br>SME-PS: Interviews with subject matter experts employed in the private sector.<br>SME-CS: Interviews with subject matter experts employed in the academic or civil sectors.</p> <p> </p> <p><strong>Full report</strong></p> <p>A report containing more information about this dataset and about the findings of our study can be found on SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525</a></p> <p> </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>
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
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.