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409 results for “information use”
Information on the Use of Anti-embolic Stockings in Patients Undergoing Vein Surgery
ClinicalTrials.gov study NCT07044739. IPD Sharing: YES. Countries: 0. Publications: 0.
Improving Anxiety Detection in Pediatrics Using Health Information Technology
ClinicalTrials.gov study NCT02562248. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A Study to Obtain Additional Information on the Use of CorMatrix® CanGaroo ECM® Envelope
ClinicalTrials.gov study NCT02418143. IPD Sharing: Not stated. Countries: 0. Publications: 0.
GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS V1
The GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS dataset is a subset from the global 30-minute PERSIANN-CCS files generated in near-real time selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign. The main goal of IFloodS were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This PERSIANN-CCS data product is available in ASCII and netCDF-4 formats from April 1, 2013 thru July 1, 2013.
Autonomous Decision Making for Planetary Rovers Using Diagnostic and Prognostic Information
Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making.We demonstrate our implementation of this architecture on a simulated rover testbed.
Malicious Large Language Models Detection using Metadata Information
<p># Introduction<br># This is the replication package for the paper "Malicious Large Language Models Detection using Metadata Information".</p> <p><br>## Task Definition<br>Given the information of LLM, the task is to identify whether it is a malicious LLM that may attack software systems. We treat the task as binary classification (0/1), where 1 stands for malicious LLMs and 0 for malicious-free LLMs.</p> <p><br>## The static of dataset<br>### Data Format<br>Before preprocessing dataset, each line in the uncompressed file represents multiple metadata of one large language model (LLM). One row is illustrated below.<br> - **idx:** the index of example<br> - **repo_id:** the id of LLM (e.g., microsoft/codebert-base)<br> - **tags:** the tags of LLM<br> - **pipeline_tag:** the pipeline_tag of LLM<br> - **downloads:** the number of downloads<br> - **created_time:** the created time of LLM<br> - **modelCard:** the text content of LLM<br> - **num_discussion:** the number of discussions<br> - **discussion:** the discussions of LLM<br> - **para_size:** the size of LLM<br> - **tensor_type:** the type of LLM<br> - **num_commit:** the number of commits<br> - **commit:** the commit of LLM</p> <p>After preprocessing dataset, you can obtain three .csv files, i.e. train.csv, valid.csv, test.csv<br> - **idx:** the index of example.<br> - **repo_id:** the id of LLM (e.g., microsoft/codebert-base).<br> - **tags:** the tags of LLM.<br> - **pipeline_tags:** the pipeline_tag of LLM.<br> - **created_time:** the created time of LLM.<br> - **model_size:** the size of LLM.<br> - **Tensor_type:** the type of LLM.<br> - **is_model_card:** Whether to include model card. If the model has model card, the value is 1, otherwise, the value is 0.<br> - **malicious_model_card:** Whether to include keywords describing the malicious model in model card. If the model card has malicious keywords, the value is 1, otherwise, the value is 0<br> - **repository_link:** Whether to include repository link: GitHub link, Arxiv link, homepage link, bugs link and issues link in model card. If the model card has link, the value is 1, otherwise, the value is 0.<br> - **dataset_info:** Whether to include the adopted dataset information in model card. If the model card has dataset information, the value is 1, otherwise, the value is 0.<br> - **metrics_info:** Whether to include the evaluated metrics information in model card. If the model card has evaluation metrics information, the value is 1, otherwise, the value is 0.<br> ?- **script_info:** Whether to include script information. If the model has script information, the value is 1, otherwise, the value is 0.<br> - **config_content:** The content of the configuration script file. This value is string type.<br> - **stakeholder_name:** The name of authors, contributors, and maintainers. This value is string type.<br> - **number_discussion:** The number of discussion. <br> - **num_pr:** The number of pull request.<br> - **malicious_discussion:** Whether the discussion contains malicious behavior keywords. If the discussion has malicious behavior keywords, the value is 1, otherwise, the value is 0.<br> - **number_commit:** The number of commit.<br> - **malicious_commit:** Whether the title and message of commits contain malicious behavior keywords. If the commit has malicious behavior keywords, the value is 1, otherwise, the value is 0.<br> - **z_download:** The z-score of number of download.<br> - **z_like:** The z-score of number of likes.</p> <p><br>### Data Statistics<br>Data statistics of the dataset are shown in the below table:</p> <p>| #File Names | #Examples |<br>| ------------------- | :------------------------: |<br>| dataset_feature.csv | 578,502 (560,257/18,245) | <br>| train_imbalance.csv | 462,801 (448205/14596) |<br>| valid_imbalance.csv | 57,849 (56025/1824) |<br>| test_imbalance.csv | 57,852 (56027/1825) |<br>| train_balance.csv | 29192 (14596/14596) |<br>| valid_balance.csv | 3648 (1824/1824) |<br>| test_balance.csv | 3650 (1825/1825) |<br>| train_imbalance_50.csv | 289252 (280129/9123) |<br>| train_imbalance_60.csv | 347101 (336154/10947) |<br>| train_imbalance_70.csv | 404952 (392180/12772) |</p> <p>29646 models contain github link<br>all_dataset.csv 596383<br>all_dataset_information.csv 589140 (safe_dataset.csv(570549), unsafe_dataset.csv(18591))<br>safe_dataset_information.csv (559582), unsafe_dataset_information.csv (18212)</p> <p>Description Feature: 'malicious_model_card', 'repository_link', 'dataset_info', 'metrics_info', 'config_conteng'<br>Stakeholder Feature: 'stakeholder_name'<br>Event Feature: 'num_pr', 'number_commit', 'malicious_commit',<br>Context Feature: 'z_download', 'z_like'</p> <p><br>## Pipeline-MPTMHunter<br>We also provide a pipeline that fine-tunes [MPTMHunter](https://doi.org/10.5281/zenodo.12578531) on this task.</p> <p>### Experimental environment configuration<br>```bash<br>huggingface_hub 0.23.1<br>libxgboost 2.0.3<br>lightgbm 4.3.0<br>networkx 3.2.1<br>nltk 3.8.1<br>numpy 1.26.3<br>openssl 3.0.13<br>pandas 2.2.1<br>pillow 10.2.0<br>scikit-learn 1.4.2<br>scipy 1.13.0<br>torch 2.3.0+cu118<br>torchaudio 2.3.0+cu118<br>torchvision 0.18.0+cu118<br>tqdm 4.66.2<br>transformers 4.37.2<br>xgboost 2.0.3<br>```</p> <p>### Dataset Collection Script<br>```bash<br>python ./script/DataExtraction.ipynb<br>python ./script/dataset_spider.py<br>python ./script/config_crawl.py<br>```</p> <p>### Dataset Preprocess Script<br>```bash<br>python feature_generation.py --input_file='../dataset/dataset_information.csv' --output_file='../dataset/dataset_feature.csv'</p> <p>python feature_generation.py --input_file='../dataset/real_world_dataset_information_0701.csv' --output_file='../dataset/real_world_dataset_feature_0701.csv'<br>```</p> <p>### Model Training Script<br>```bash<br>python run_codet5_lstm.py --output_dir='../saved_models/codet5_lstm_imbalance_final' --model_type=codet5 --tokenizer_name='../models/codet5' --model_name_or_path='../models/codet5' --do_train --train_data_file='../dataset/train_imbalance_70.csv' --eval_data_file='../dataset/valid_imbalance_70.csv' --test_data_file='../dataset/test_imbalance.csv' --epoch=3 --block_size=510 --train_batch_size=64 --eval_batch_size=64 --learning_rate=2e-5 --max_grad_norm=1.0 --evaluate_during_training --seed=123456<br>```</p> <p>### Model Inference Script<br>```bash<br>python run_codet5_lstm.py --output_dir='../saved_models/codet5_lstm_imbalance' --model_type=codet5 --tokenizer_name='../models/codet5' --model_name_or_path='../models/codet5' --do_eval --do_test --train_data_file='../dataset/train_imbalance_70.csv' --eval_data_file='../dataset/valid_imbalance_70.csv' --test_data_file='../dataset/test_imbalance.csv' --epoch=3 --block_size=510 --train_batch_size=64 --eval_batch_size=64 --learning_rate=2e-5 --max_grad_norm=1.0 --evaluate_during_training --seed=123456<br>```</p> <p>### Evaluation Script<br>```bash<br>python ../evaluation/evaluation.py -a ../dataset/test_balance.csv -p ../saved_models/codebert_imbalance_all/predictions.txt<br>python ../evaluation/evaluation.py -a ../dataset/test_imbalance.csv -p ../dataset/predictions.txt<br>```</p> <p>## Result<br>The results on the test set are shown as below (We use the OpenTextClassification as the baseline):</p> <p>| Methods | ACC | Precision | Recall | F1-Score |<br>| Random Forest | 97.18% | 83.80% | 13.04% | 22.57% |<br>| LR | 96.80% | 43.46% | 4.55% | 8.23% |<br>| LightGBM | 97.62% | 95.18% | 25.97% | 40.81% |<br>| TextRNN | 96.86% | 73.33% | 0.60% | 1.20% |<br>| TextCNN | 98.89% | 95.61% | 68.00% | 79.47% |<br>| TextRCNN | 98.87% | 95.36% | 67.62% | 79.13% |<br>| TextRNN_Att | 98.94% | 95.29% | 69.86% | 80.62% |<br>| MPTMHunter | ** 99.99% ** | ** 99.95% ** | ** 99.78% ** | ** 99.86% ** |</p>
Supplementary information for "Ultrasonic single beam manipulation of particles and organoids through a petri-dish and a plastic tube using an acoustic field live-visualisation method"
<p>Test</p>
DATASET - In search of an effective source use pattern for writing argumentative and informative synthesis texts
<p>This SPSS file contains the data corresponding to the study as published in the article <em>In search of an effective source use pattern for writing argumentative and informative synthesis texts </em>(Vandermeulen, Van den Broek, Van Steendam, & Rijlaarsdam, 2019). </p> <p> </p>
Datasets and Supporting Information for "A novel semi-analytical (inertial) solution for determining permeability of highly pervious porous materials using the two-reservoir laboratory setup"
<p>The uploaded datasets represent the experimental data of the Two-Reservoirs test and the Constant Head Permeability Test, both described in details in the paper "A novel semi-analytical (inertial) solution for determining permeability of highly pervious porous materials using the two-reservoir laboratory setup" and in the corresponding uploaded pdf document "FStanic_et_al_SupportingInformation2.pdf". Data for the Two-Reservoirs test contain information about the water level values <em>Π</em><em><sub>1</sub></em> and <em>Π</em><em><sub>2 </sub></em>in connected Reservoirs 1 and 2, respectively, and times when the corresponding values are captured, while data for the Constant Head Permeability test contain water level values recorded in Piezometer (<em>Π</em><em><sub>12</sub></em>)<em> </em>and the corresponding water flux values<em> v<sub>s</sub>. </em></p> <p>Also, in the document "FStanic_et_al_SupportingInformation1.pdf" are presented some technical details related to the experimental apparatus developed in this work, that can be helpful for researchers trying to conduct similar test. </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.