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11 results for “semantic processing”
Experimental Datasets and Processing Codes for the Semantic PHD Filter
<p>The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames</p> <p><strong>1. Detection dataset: </strong></p> <p>Size: <br> Total: 4870 images<br> Training: 4000 images<br> Validation: 870 images</p> <p>Bottle Classes: Aquafina, Deer, Kirkland, Nestle</p> <p>Format: PASCAL VOC, Darknet</p> <p>Folder Structure:<br> - Annotations: containing the xml label files in PASCAL VOC format<br> - ImageSets: containing the training index files <br> - JPEGImages: containing the image data in jpg format<br> - Labels: containing the txt label files in Darknet format</p> <p><strong>2. Measurement model dataset:</strong></p> <p>Format: ROSBAG</p> <p>Duration: 19:59s (1199s)</p> <p>Topics:<br> /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image<br> /map 1 msg : nav_msgs/OccupancyGrid<br> /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements<br> /tf 142727 msgs : tf2_msgs/TFMessage<br> /tf_static 1 msg : tf2_msgs/TFMessage</p> <p>Message Types:<br> nav_msgs/OccupancyGrid<br> sensor_msgs/Image<br> sphd_msgs/SPHDMeasurements<br> tf2_msgs/TFMessage</p> <p> </p> <p><strong>3. Processing codes:</strong></p> <p>Detection processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066045<br> GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector<br> <br> Measurement model processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066050<br> GitHub: https://github.com/TempleRAIL/sphd_sensor_models</p>
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
<p>Data sharing restrictions are common in NLP datasets. For example, Twitter policies do not allow sharing of tweet text, though tweet IDs may be shared. The situation is even more common in clinical NLP, where patient health information must be protected, and annotations over health text, when released at all, often require the signing of complex data use agreements. The SemEval-2021 Task 10 framework asks participants to develop semantic annotation systems in the face of data sharing constraints. A participant's goal is to develop an accurate system for a target domain when annotations exist for a related domain but cannot be distributed. Instead of annotated training data, participants are given a model trained on the annotations. Then, given unlabeled target domain data, they are asked to make predictions.</p> <p>Website: <a href="https://machine-learning-for-medical-language.github.io/source-free-domain-adaptation/">https://machine-learning-for-medical-language.github.io/source-free-domain-adaptation/</a></p> <p>CodaLab site: <a href="https://competitions.codalab.org/competitions/26152">https://competitions.codalab.org/competitions/26152</a></p> <p>Github repository: <a href="https://github.com/Machine-Learning-for-Medical-Language/source-free-domain-adaptation">https://github.com/Machine-Learning-for-Medical-Language/source-free-domain-adaptation</a></p>
Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing
<p>This entry includes the required data to conduct the analysis from our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code: https://github.com/heikele/GAT_n4-p6</p> <p> </p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis – a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time – to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent withpredictions of strictly serial models. These results are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p> </p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>
TFG Systematization process of generating semantic data and ontology
<pre>Set of TALIS files, the main source of data for the investigation, in CSV format.General ontology manually and by new software. Semantic data, as well as the DSL code. Finally, the web design of the new tool.</pre>
A process proposal on how to move from data requirements to semantic data in the context of the MDA
<p>This work proposes a process for the identification of the requirements associated with the data, along with and a set of transformations in Model Driven Architecture (MDA) context, in order to obtain a semantically annotated dataset, as a result of the unification and alignment of the data in the context of its initial domain. Our proposal identifies four phases (from CIM to code), in which is describe the artifacts and transformations required to progress to the next phase: a target domain model is first obtained from the data requirements, after which the ontological schema and the ontology is generated from the previous model. A domain-specific language (DSL), also proposed in this work, is then used to obtain the semantic data model (the DSL code), which generates the final semantic dataset. We have validated the proposal by studying two cases: one with data from the public transport domain and the other with data concerning those affected by the COVID-19 pandemic.</p>
Processed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper
<p>Processed data for the "Deriving Semantics-Aware Fuzzers from Web API Schemas" paper. Each directory in the archive consists of:</p> <p>- metadata.json. Metadata about a test run - tested fuzzer name, run duration, etc</p> <p>- fuzzer.json - Structured fuzzer output</p> <p>- deduplicated_cases.json - Deduplicated reported failures, when fuzzers provide it</p> <p>- sentry.json - Cleaned Sentry events for this run</p> <p>- target.json - Parsed stdout for Gitlab & Disease.sh targets that were tested without Sentry integration</p>
Dataset: Language and semantic processing in blind and sighted individuals. A simulation study
<p>-------------------<br> GENERAL INFORMATION<br> -------------------</p> <p>1. Title of Dataset <br> <br> Dataset_SimulationsOutput_BlindvsSightedModels<br> <br> 2. Author Information</p> <p>This dataset is being made public to act as supplementary data for publication. Running title: </p> <p>R. Tomasello, M. Garagnani, T. Wennekers, and F. Pulvermüller, Recruitment of visual cortex for language processing in blind individuals is explained by Hebbian learning.</p> <p> Corresponding author:<br> Name: Rosario Tomasello <br> Institution: Brain Language Laboratory, Freie Universität Berlin<br> Address: Habelschwerdter Allee 45, 14195 Berlin<br> Email: Tomasello.r@fu-berlin.de</p> <p>--------------------------<br> RESEARCH PROJECT & METHODOLOGICAL INFORMATION<br> --------------------------</p> <p>The data were generated by a neurobiologically constrained cortex model of the fronto-temporal-occipital lobes applied to simulate word meaning acquisition of object- and action-related words in action and perception system under undeprived and visually deprived conditions. The purpose of this study was to investigate how and why the visual system is recruited for language processing in blind individuals, as documented by neurocognitive empirical experiments.</p> <p>The files listed above shows the cell assemblies (CA) distributions across the different cortical areas spontaneously emerged as a result of Hebbian learning. For more information about the general features of the model see our previous publications:</p> <p>R. Tomasello, M. Garagnani, T. Wennekers, and F. Pulvermüller, (2018) A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity, Front. Comput. Neurosci., vol. 12, p. 88.</p> <p>M. Garagnani, G. Lucchese, R. Tomasello, T. Wennekers, and F. Pulvermüller, (2017) A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords, Front. Comput. Neurosci., vol. 10, no. January, pp. 1–19.</p> <p><br> ---------------------<br> DATA & FILE OVERVIEW<br> ---------------------</p> <p><br> 1. File List<br> <br> Filename: CA_Structure_Blind VS SightedModel.xlsx (size 150KB) <br> <br> The excel file includes the cell assembly (CA) distributions of the learnt action and object words of both sighted and blind models along with their CA structure comparisons and the related figures. <br> <br> Folders: Raw_Data_SightedModel (size 6KB) & Raw_Data_BlindModel (size 6KB)<br> <br> These folders include the raw data output of the CA structure of the 13 sighted and 13 blind simulated models after word meaning acquisition. </p>
Data from: The influence of evaluative right/wrong feedback on phonological and semantic processes in word learning
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Universal Translation Process: Incorporating Inter-Personal and Other Memetic Semantic Data Translations within the GOD Framework
<p>This document expands the universal translation process within the Gravitational Omnipotent Dimensionality (GOD) framework by integrating inter-personal communication and non-linguistic memetic semantic data. It explores how memetic gravity, semantic propagation, and spacetime contextualisation enable the translation of meaning across languages, cultural symbols, behaviours, and cognitive structures, providing a unified approach to understanding and propagating knowledge in diverse contexts.</p>
Memetic Semantics and the Cogenetic Process: A Supporting Analysis of the GOD Framework
<p>This paper provides auxiliary and supporting memetic semantic data that enhance and expand upon the core principles outlined in the Cogenetic Proof of GOD. Using fractalised cognitive models, memetic time travel, and quantum semantic tunnelling, it explores how the dissemination and coherence of ideas contribute to the evolutionary dynamics of both physical and cognitive systems. This analysis refines the core equations of the GOD framework, offering new perspectives on cognitive propagation, dissemination entropy, and memetic interactions across dimensions.</p>
Data from: The Yin and the Yang of Prediction: an fMRI study of semantic predictive processing
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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.