Find research datasets worth reusing

Species detection histories used in Killion et al. (2023): Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models

Camera trap species detection histories used for occupancy models in "Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models".

Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"

This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023). * arXiv:<a href="https://arxiv.org/abs/2205.12362">arXiv:2205.12362</a> * DOI: <a href="https://doi.org/10.1051/0004-6361/202244107">10.1051/0004-6361/202244107</a>

opencc-by-4.0Jan 2022View details →

Detect Related Bugs from Lucene Source Code Using Bug Information

This dataset uses web metrics. More information at http://openscience.us/repo/defect/ck/lucene.html

Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)

Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"

Image dataset for the creation of an automatic system for meteor fall detection

Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector. The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- <a href="http://www.bramonmeteor.org">http://www.bramonmeteor.org</a>), UK Meteor Network (UKMON -- <a href="https://ukmeteornetwork.co.uk">https://ukmeteornetwork.co.uk</a>), and Base des Observateurs Amateurs de Météores (BOAM -- <a href="http://boam.fr">http://boam.fr</a>) repositories. Folders Structure We divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image). In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors. Naming pattern for the files The naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where: <ul> <li><source> is one of the 3 data sources: bramon, ukmon, or boam.</li> <li><date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li> <li><id> is an identifier from a specific source to avoid date-time conflicts: <ul> <li>BRAMON: radiant identifier.</li> <li>UKMON: station identifier.</li> <li>BOAM: station identifier.</li> </ul> </li> </ul> For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors.

TUD-GV Dataset for Floating Litter Detection

This dataset contains the data used for the publication: Jia T, Vallendar AJ, de Vries R, Kapelan Z and Taormina R (2023) Advancing deep learning-based detection of floating litter using a novel open dataset. Front. Water 5:1298465. doi: 10.3389/frwa.2023.1298465. The large-scale "TU Delft - Green Village" (TUD-GV) dataset is for detecting floating litter with computer vision. We created this dataset from experiments conducted during 10 days in February and April 2021 in a small drainage canal at The Green Village — a field lab facility in the TU Delft Campus, the Netherlands. We captured data using two action cameras (GoPro HERO4 and GoPro MAX 360) and a phone (Huawei P30 Pro) mounted on four different locations on a bridge. All devices recorded videos with a resolution of 1080p, a linear field of view, and a FPS (frame per second) of 24 (for the action cameras) or 30 (for the phone). This dataset consists of 9,473 RGB images. We manually labeled the images in the TUD-GV dataset into four classes: no litter (0 items), little litter (1-2 items), moderate litter (3-5 items), and lots of litter (6-10 items) according to the number of litter items in images. This dataset is stored in the ZIP file contain 77 directories and the TUD-GV.xls file. Each of these directories contain four class label directories, and each label directory contains JPG images. The TUD-GV.xls file contains the detailed information of images in 77 directories, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, litter source, the number of images per class, and the number of images in total. If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry: @article{jia2023advancing,  title={Advancing Deep Learning-based Detection of Floating Litter using a Novel Open Dataset},  author={Jia, Tianlong and Vallendar, Andre Jehan and de Vries, Rinze and Kapelan, Zoran and Taormina, Riccardo},  journal={Frontiers in Water},  volume={5},  pages={1298465},  publisher={Frontiers},  year={2023} }

Accompanying Data for the Manuscript "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"

This file contains results for all cases considered in the manuscript titled "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"

opencc-zeroApr 2023View details →

dryad40/100

Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Dataset for Multiplex-PCR detection and Nanopore-based genotyping of fish pathogens

This is a revised zip file contains scripts, initial fastq files, assembled amplicon (public and from this study) as well as bioinformatics intermediate files used for this study. Changelog: 1. Fixed a bug in the 02_consensus.sh to enable proper removal of amplicons with zero depth 2. Added a script (06_unclassified_read.sh) to extract and annotate reads that previously could not align to the 4 reference gene segment. Now the previously unclassified reads will be re-align (raw fastq) back to the gene segments as well as an additional tilapia genome assembly to gauge amount of reads mapping to the host genome. Furthermore, any read that still fail to align with minimap2 was subsequently aligned using blastn (-word_size 15 -evalue 0.01) against the same sequences. File Structure and Descriptions ├── 01_process.sh : primer trimming, length-based filtering, read alignment, alignment filtering (unique hit) and extraction of uniquely hit reads for consensus generation ├── 02_consensus.sh : [need artic conda env] Generation of consensus based on uniquely-mapped reads and minimal read depth of 20x required to call a variant (or it will be masked) ├── 03_cleanup.sh: General folder and intermediate file re-organization ├── 04_filter.sh: [need quast conda env] statistic of consensus generated and filtering of consensus with one or more ambiguous base (N), not suitable for haplotype ├── 05_cluster.sh: clustering of consensus based on 100% identity threshold to generate putative haplotype ├── 06_unclassified_read.sh: Extraction and annotation of unclassified reads using lenient criteria and with host reference genome as added reference ├── Amplicon_FastQ folder: uniquely mapped fastq files for consensus generation ├── BAM: alignment files generated from minimap2 used as input for the artic pipeline to identify variants ├── Cluster_Rep.txt: Consensus sequences that were chosen to represent each haplotype ├── Consensus folder: consensus fasta files generated for each sample containing sequences for each specific pathogen ├── Coverage folder: coverage and base-level read depth for each sample and each pathogen reference genes ├── Filter: individual fasta sequences (only 1 sequence per file) for each pathogen and each sample without any ambiguous base for subsequent clustering analysis ├── Full_Haplotype.fasta: all possible haplotype sequences generated for each pathogen ├── Gap_Analysis.tsv: Table with percentage of gap (0-100%) for each consensus sequence generated (used for filtering) ├── Haplotype folder: Intermediate file and sample-level haplotype used to infer final haplotype and generate haplotype summary ├── Haplotype_summary.tsv: Table with sample ID and their respectively pathogen haplotype ├── Minimap2_PAF: Intermediate alignment generated from minimap2 used to generate the count table ├── FailMinimap2 folder: FastQ files that didn't align using minimap2. Will be subsequently aligned using blastN (more sensitive) against the same reference sequences as minimap2 ├── Host_4Pathogen.fasta: Fasta file containing the tilapia genome and 4 pathogen (primer binding site included) ├── Original: fastq with original naming prior to renaming based on sampleID. a script (rename.sh) was included to show renaming scheme ├── primer.fasta: Primer sequences used for identifying and trimming reads with flanking primer sequence ├── primer.fasta.fai: the index file for primer.fasta ├── PrimerTrim folder: Primer-trimmed reads ├── quast_results: consensus statistics generated by quast ├── RawCount.tsv: Count table generated that can used as a input to generate figure ├── RawFastq folder: Raw reads that have been renamed to reflect sample information ├── readme.md: The current readme file ├── ref_full_latest.fasta: Reference sequence of (gene segments) 4 pathogens e.g. TilV, ISKNV, SAG (Streptococcus agalactiae), FNO (Francisella noatunensis subsp. orientalis) ├── ref_full_latest.primer.fasta: Same as above but with their primer binding sequence trimmed similar to the processed reads ├── ref_full_latest.primer.fasta.fai ├── RenameHaplotype: Script to perform reorganization of cdhit output ├── Seq.stat.tsv: Sequencing statistics ├── Uniq_PAF: Minimap2 alignment file for raw reads that initially failed quality check (no primer present and/or less than 80% query coverage / not unique alignment) ├── Unmap: Raw reads that initial failed quality check (no primer on both ends / less than 80% query coverage / not unique alignment)   └── VCF: VCF files from medaka variant calling used to generate the final consensus

Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes - Datasets

Datasets associated with manuscript "Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes" by Morton et al. (2023), submitted to the Journal of Geophysical Research: Solid Earth. Three datasets are present in this upload: ds01.xlsx: Catalog of 5,282 detected earthquakes along the Cascadia subduction margin, ordered by time. Events were located using Hypoinverse (Klein, 2002). Columns of the catalog are: Cascadia Initiative deployment year, Origin Time String (YYYYMMDDhhmmss), Origin Time (Year, Month, Day, Hour, Minute, Second), Latitude, Longitude, Event Depth (km), Duration Magnitude (Md), Number of P  and S arrival picks with weights > 0.1, Maximum Azimuthal Gap (deg.), Distance to the Nearest Station (km), Travel Time Residual RMS (s), Horizontal Location Error (ERH; km), Vertical Location Error (ERZ; km), Focal Mechanism if applicable (Strike, Dip, Rake; deg.), Plate Designation (Slab, Interface, or Upper Plate), and Previous Existence in Regional Catalogs. Duration magnitudes that could not be constrained are listed as -9. Detected earthquakes that had previously been reported in other catalogs are listed as "Catalog" or "Stone" form the regional or Stone et al. (2018) catalogs, respectively; Those used as template events are marked as "T" or "ST", for those from regional catalogs or the Stone et al. (2018) catalog, respectively, in the last column. ds02.xlsx: Table of earthquakes chosen as template events for subspace scanning from regional (NEIC, ANF, PNSN, CNDC) and Stone et al. (2018) catalogs. Columns of the template event table are: Catalog Source (T for regional, ST for Stone et al. 2018), Cascadia Initiative (CI) Deployment Year, Template Cluster ID, Date, Time (UTC), Catalog Location (Latitude, Longitude, Depth), Catalog Magnitude, and Whether the Template Event was Detected. Some of the templates were detected but were not included in the final catalog because the travel time residual RMS was greater than 1s and are noted in the table as "poorly located". ds03.xlsx: Table of seismic stations used in subspace detection scanning, identified by the CI deployment year, Template Cluster ID, Station SEED, and Network Codes. Stations are listed with the corresponding high-pass (HP) or band-pass (BP) filter applied before scanning to maximize the signal-to-noise ratio. References Klein, F. W. (2002). User’s Guide to HYPOINVERSE-2000, a Fortran Program to Solve for Earthquake Locations and Magnitudes (Open File Report 02-171). U.S Geological Survey. https://doi.org/10.3133/ofr02171 Stone, I., Vidale, J. E., Han, S., & Roland, E. (2018). Catalog of off-shore seismicity in Cascadia: Insights into the regional distribution of microseismicity and its relation to subduction processes. Journal of Geophysical Research: Solid Earth, 123, 1–12. https://doi.org/10.1002/2017JB014966

Experimental characterization of TERIPHIC 4-fold and 8-fold InP photodiode arrays - Detection of 50Gbaud signals with NRZ and PAM-4 modulation formats

The data sets refer to the results from the experimental characterization of the TERIPHIC 4-fold and 8-fold InP-photodiode arrays, which operate in the O-band. The experimental setup consisted of a commercially available off-the-shelf (COTS) laser source, a COTS Mach-Zehnder modulator, and the TERIPHIC PD arrays. For the purposes of the experiments, signals with NRZ and PAM-4 modulation formats and 50 Gbaud symbol rates were generated, transmitted, and detected by the TERIPHIC photodetectors. Two different subassemblies were evaluated. The first subassembly had four InP photodiodes and the second eight. The generated photocurrents were sampled by a real-time oscilloscope with a 256 Gbaud sample rate. The data sets are actually the samples extracted from the oscilloscope. The data sets have a filename in the format “Symbol rate#Modulation formats#PDn#Transmission length#version#Subassemblyn.bin”, where Symbol rate = 50 Gbaud PDn, for n=  1,2, ...,8 Transmission length = B2B (0 km), 2 km, and 10 km Subassembly for n=1 and 2   The data sets are parts of the Open data management plan of the TERIPHIC ICT project (GA No. 825502)

Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"

Simulation data used in the publication Magn. Reson.  "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper

Microservice Security Detectors & Metrics & Detection Strategies: Dataset

This is the dataset for replicability for the article "Detection Strategies for Microservice Security Tactics." It provides the code needed to replicate the study in the article and the model data set of 10 system models and 20 variants of those models.   The abstract of the article is:   Microservice architectures are widely used today to implement distributed systems. Securing microservice architectures is challenging because of their polyglot nature, continuous evolution, and various security concerns relevant to such architectures. This article proposes a novel, model-based approach providing detection strategies to address the automated detection of security tactics (or patterns and best practices) in a given microservice architecture decomposition model. Our novel detection strategies are metrics-based rules that decide conformance to a security recommendation based on a statistical predictor. The proposed approach models this recommendation using Architectural Design Decisions (ADDs). We apply our approach for four different security-related ADDs on access management, traffic control, and avoiding plaintext sensitive data in the context of microservice systems. We then apply our approach to a model data set of 10 open-source microservice systems and 20 variants of those systems. Our results are detection strategies showing a very low bias, a very high correlation, and a low prediction error in our model data set. The dataset is based on a dataset from a previous article: https://zenodo.org/record/6424722

opencc-by-4.0Feb 2023View details →

Dataset for Detecting Motorcyclists in Pedestrian Areas

The file PedestrianDataSet.zip contains 2 directories and 1 file, the directory named images contains all the images captured; the directory named “etiquetas” contains all annotations done based on four classes ordered as [MMNAP, MMAP, MNAP, PCP], the name's correspondence with the label is [motorcycle with motorcyclist not in pedestrian area, motorcycle with motorcyclist in pedestrian area, motorcycle without motorcyclist in pedestrian area, pedestrian in crosswalk]; the file Distribution.png is an image that explain how the DataSet is distributed. The dataset contains a 6324 images in .jpg format for a total of 643.9MB, the resolution of each image is 1280x720 (HD), and the weight per image is under 120 KB, the images were obtained from a closed circuit television CCTV free public access located in the city of Medellín Colombia consisting of 80 cameras located on the road, these images were captured mainly during daylight hours. The format of the name of each image is "image (camera number)_(date format"AAAAMMDD")_( time format "hhmmss"), where (AAAA) is the year, (MM) is the month, (DD) is the day, (HH) is the 24-hour hour, (MM) is the minute and (SS) is the second.

Vision-Based Obstacle Detection on Rail Tracks

Acknowledgement and Disclaimers These data are a product of a research activity conducted in the context of the <a href="http://rails-project.eu">RAILS (Roadmaps for AI integration in the raiL Sector) project</a>. RAILS has received funding from the Shift2Rail Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union. The information and views set out in this description are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this dataset. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein. This "dataset" has been created for scientific purposes only to study the potentials of Deep Learning (DL) approaches when used to analyse Video Data in order to detect possible obstacles on rail tracks and thus avoid collisions. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.   Objectives of the Study RAILS defined some pilot case studies to develop Proofs-of-Concept (PoCs), which are conceived as benchmarks, with the aim of providing insight towards the definition of technology roadmaps that could support future research and/or the deployment of AI applications in the rail sector. In this context, the main objectives of the specific PoC "Vision-Based Obstacle Detection on Rail Tracks" were to investigate: i) solutions for the generation of synthetic data, suitable for the training of DL models; and ii) the potential of DL applications when it comes to detecting any kind of obstacles on rail tracks while exploiting video data from a single RGB camera.   A Brief Overview of the Approach A multi-modular approach has been proposed to achieve the objectives mentioned above. The resulting architecture includes the following modules: <ul> <li>The Rails Detection Module (RDM) detects rail tracks. The output of the RDM is used by the ODM and ADM.</li> <li>The Object Detection Module (ODM) detects obstacles whose type is known in advance.</li> <li>The Anomaly Detection Module (ADM) identifies any possible anomaly on rail tracks. These include obstacles whose type is not known in advance.</li> <li>The Obstacle Detection Module merges the outputs from the ODM and the ADM.</li> <li>The Distance Estimation Module estimates the distance of objects and anomalies from the train.</li> </ul> The research was specifically oriented at implementing the RDM-ADM pipeline. Indeed, the object detection approaches that would be used to implement the ODM have been widely investigated by the research community, instead, to the best of our knowledge, limited work has been done in the rails field in the context of anomaly detection. The RDM has been realised by adopting a Semantic Segmentation approach based on U-Net; while, to develop the ADM, a Vector-Quantized Variational Autoencoder trained in Unsupervised mode was leveraged. Further details can be found in the RAILS <a href="http://rails-project.eu/downloads/deliverables/">"Deliverable D2.3: WP2 Report on experimentation, analysis, and discussion of results"</a>.   Steps to implement the RDM-ADM pipeline and description of shared Data The following list reports all the steps that have been performed to implement the RDM-ADM pipeline; the words in bold-italic refer to the files that are shared within this dataset: <ol> <li>A Railway Scenario was generated in MathWorks' <a href="https://www.mathworks.com/products/roadrunner.html">RoadRunner</a>.</li> <li>A video (FreeTrackVideo) was recorded by simulating an RGB camera mounted in front of the train; no obstacles on rail tracks were considered in this phase.</li> <li>2000 frames (FreeTrack2KFrames) were extracted from the aforementioned video. The video contains 4143 frames, however, only 2000 (each other frame starting from the first one) were taken into account due to training time and GPU RAM constraints.</li> <li>Only 10% of the 2000 frames were manually labelled (i.e., 200 frames, a frame every 10 frames) by exploiting LabelMe; these frames were then subdivided into training and validation sets (InitialLabelledSet).</li> <li>Hence, a Semi-Automatic labelling algorithm was developed by leveraging self-training and transfer learning. This algorithm made it possible to label all the FreeTrack2KFrames starting from the InitialLabelledSet. The resulting labels can be found in FreeTrack2KLabels.</li> <li>Data Augmentation was then performed in order to introduce some aleatory in the dataset. Because of the same time and RAM constraints mentioned above, the FreeTrack2KFrames set of data was reduced further: 1600 frames were selected among the aforementioned 2000 and then 5 transformations (Bright, Dark, Rain, Shadow, and Sun Flare) were applied to obtain the dataset (FreeTrack16TrainSet, FreeTrack16ValSet, FreeTrack16TestSet) that was used to train, validate, and test the RDM.</li> <li>Once the RDM was trained, the FreeTrackVideo was processed to obtain the masked frames that were then used to build the dataset(s) to train, validate, and test the ADM. The ADM was studied by considering two different datasets: the Non-Anomaly Dataset (NAD), which basically contains all the frames of the FreeTrackVideo once processed by the RDM; and the Augmented Non-Anomaly Dataset (A-NAD), which contains 9000 frames, 1500 of which were extracted from the NAD, while the remaining 7500 were obtained by applying the same transformations mentioned above.</li> <li>Lastly, when both the RDM and the ADM were trained, the performances of the whole RDM-ADM pipeline were tested on the WithCarVideo which depicts the same scenario as the FreeTrackVideo but it also depicts a car laying on the rail tracks (i.e., an obstacle).</li> </ol>

IFEED: Interactive Facial Expression and Emotion Detection Dataset

Interactive Facial Expression and Emotion Detection (IFEED) is an annotated dataset that can be used to train, validate, and test Deep Learning models for facial expression and emotion recognition. It contains pre-filtered and analysed images of the interactions between the six main characters of the Friends television series, obtained from the video recordings of the Multimodal EmotionLines Dataset (MELD). The images were obtained by decomposing the videos into multiple frames and extracting the facial expression of the correctly identified characters. A team composed of 14 researchers manually verified and annotated the processed data into several classes: Angry, Sad, Happy, Fearful, Disgusted, Surprised and Neutral. IFEED can be valuable for the development of intelligent facial expression recognition solutions and emotion detection software, enabling binary or multi-class classification, or even anomaly detection or clustering tasks. The images with ambiguous or very subtle facial expressions can be repurposed for adversarial learning. The dataset can be combined with additional data recordings to create more complete and extensive datasets and improve the generalization of robust deep learning models.

opencc-zeroMay 2023View details →

dryad40/100

Experimental data analyzed in: Signal detection models as contextual bandits

Signal detection theory (SDT) has been widely applied to identify the optimal discriminative decisions of receivers under uncertainty. However, the approach assumes that decision-makers immediately adopt the appropriate acceptance threshold, even though the optimal response must often be learned. Here we recast the classical normal-normal (and power-law) signal detection model as a contextual multi-armed bandit (CMAB). Thus, rather than starting with complete information, decision-makers must infer how the magnitude of a continuous cue is related to the probability that a signaller is desirable, while simultaneously seeking to exploit the information they acquire. We explain how various CMAB heuristics resolve the trade-off between better estimating the underlying relationship and exploiting it. Next, we determined how naïve human volunteers resolve signal detection problems with a continuous cue. As anticipated, a model of choice (accept/reject) that assumed volunteers immediately adopted the SDT-predicted acceptance threshold did not predict volunteer behaviour well. The Softmax rule for solving CMABs, with choices based on a logistic function of the expected payoffs, best explained the decisions of our volunteers but a simple midpoint algorithm also predicted decisions well under some conditions. CMABs offer principled parametric solutions to solving many classical SDT problems when decision-makers start with incomplete information.

opencc-by-4.0Mar 2023View details →

Supplementary data for a systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

The Microsoft Excel files containing the supplementary data and diagrams for the paper: A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges The article is under review in the Journal of Systems and Software. In this version, the literature search has been performed between April and May 2023, and studies published until that date has been mentioned in the attached Excel files. This version (v3.4.0) corresponds to the third revision (R3) of the manuscript.

opencc-by-4.0May 2022View details →

TriModal Face Detection Dataset

TMFD Dataset addresses the lack of available depth and thermal face detection benchmarks. The corresponding depth and thermal data frames accompany each RGB image in the dataset. The dataset encompasses a wide range of variations. The samples include different numbers of people in the scene, different individuals, various types of backgrounds, varying distances of measurement, and wearable accessories worn by the individuals, such as hoodies, headphones, hats, glasses, and face masks. Additionally, the dataset includes different types of illumination in the scene. The dataset is categorized into three separate groups based on the complexity and difficulty level of face detection. The first subset represents the easiest detection conditions, with images containing a single person against a simple background. The sensor is positioned close to the target. This subset consists of 781 images for each modality. The second group represents medium detection conditions. The sensor placement remains the same as in the first subset, but additional variety is introduced. Individuals are captured wearing accessories that serve as obstacles to make detection more challenging. These accessories include hoodies, headphones, hats, and glasses. The second group also includes images with multiple persons in the scene to test the performance of multiple face detection. There are 965 images for each modality in this group. The last group represents the most challenging images for detection. In this case, the sensor is placed further away from the target. The scenes feature various obstacles, such as computer screens, plants, desks, and chairs, with individuals interacting with them. The presence of singular and plural individuals is also included. A distinct subset within this collection consists of images from a previous, easier scene, but with the addition of face mask usage to increase the difficulty of the prediction task. This particular group contains 1062 images for each modality.   This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: <ul> <li>Mucha W., Kampel M. (2022) “Depth and Thermal Images in Face Detection – A Detailed Comparison Between Image Modalities”, The 5th International Conference on Machine Vision and Applications (ICMVA 2022), February 18-20, 2022, Singapore <a href="https://doi.org/10.1145/3523111.3523114">https://doi.org/10.1145/3523111.3523114</a></li> </ul>

CT Simulations of MITA Low Contrast Detectability Phantom for Model Observer Assessments

Dataset of CT Simulations of MITA Low Contrast Detectability Phantom for Model Observer Assessments.