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831 results for “automaticity”
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning
Open the record for dataset details and reuse information.
2-minute PALMOS automatic weather station meteorological measurements (precipitation, radiation, cloud base, temperature, etc.) from Palmer Station Antarctica, 2001 - March 2017.
Automated weather observation station at Palmer Station Antarctica using Coastal Environment sensors. Started recording in November 2001. Overlapped for two years with manual observations. In December 2003, the PalMOS automated report generator was brought online and the manual observations discontinued. The sole manual observation remaining is the daily snow-level and new snow level measurements.
Benchmark for Automatic Glottis Segmentation (BAGLS)
<p>BAGLS is a benchmark dataset intended to compare performance across automatic glottis segmentation methods.</p>
Datasets for "Automatic Design of Mechanical Metamaterial Actuators"
<p><strong>Publication</strong></p> <p>arXiv preprint: <a href="https://arxiv.org/abs/2002.03032">https://arxiv.org/abs/2002.03032</a></p> <p>journal version: [add link to journal]</p> <p> </p> <p><strong>Code</strong></p> <p><a href="https://github.com/ComplexityBiosystems/metamech">https://github.com/ComplexityBiosystems/metamech</a></p> <p><a href="https://github.com/complexitybiosystems/metamech_datasets">https://github.com/complexitybiosystems/metamech_datasets</a></p> <p> </p> <p><strong>Datasets</strong></p> <p>Figure 2:</p> <ul> <li><em>antiparallel_{human,machine}_design.stl</em>: STL files for human design and machine design configurations performing antiparallel movement.</li> </ul> <p>Figure 3: </p> <ul> <li><em>crane_scaling_dataframe.csv: </em>raw efficiency values and execution times used to generate Figure 3</li> </ul> <p>Figure 4:</p> <ul> <li><em>eta_random.dat</em>, <em>eta_triang.dat, plot_distrib.gnupot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel d</li> <li><em><em>eta_vs_displ_random.dat</em>, <em>eta_vs_displ_triang.dat</em></em><em>, eta_vs_displ.gnuplot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel c</li> <li><em>structures.tar.bz2</em>: all the structures relative to Figure 4 panel d, in a format suitable for visualization with ovito software</li> </ul> <p>Figure 5: data files for human and machine generated pairs of pliers in a format suitable for visualization with ovito software. "*.agr" and "*.data" files can be opened with any text editor to directly visualize the raw data. </p> <ul> <li><em>*_rest.data:</em> rest configurations which are represented in grey in Fig. 5a and Fig. 5b. </li> <li><em>*_F0.01.data</em>: displaced configurations with input force Fext = 0.01 and output spring stiffness kext = 0.01, also reported in Fig. 5a and Fig. 5b.</li> <li><em>*.agr</em>: dataset for Figure 5c and 5d in xmgrace format.</li> </ul> <p>Figure 6:</p> <ul> <li><em>Fig6/Fig6b/*.csv</em>: raw values of efficiency used to generate Figure 6 panel b</li> <li><em>Fig6/</em><em>Fig6c-training-data/*/*.png: </em>a total of 1.163.733 PNG images, of size 192 x 168 pixels, as described in the methods section of the manuscript, organized in directories corresponding to different runs.</li> <li><em>Fig6/Fig6c-training-data/metadata.csv: </em>maps each image path to its associated efficiency value</li> <li><em>Fig6/Fig6c-efficiency-data/eta_{CNN,DEM}.txt</em>: raw values of efficiency used to generate Figure 6 panel c.</li> </ul> <p>Figure 7:</p> <ul> <li><em>Fig7/transfer_learning_regression_values.csv: full raw data, including regression coefficients, p-values, slopes and intercepts corresponding to Figure 7 panels i and j</em></li> </ul> <p><em>Figure S3:</em></p> <ul> <li><em>orthogonal_{human,machine}_design.stl: STL files for human design and machine design configurations performing orthogonal movement.</em></li> </ul> <p> </p>
An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. An emerging strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. The presence of test smells in unit test code could be used as an indicator of unit test quality. In this paper, we present an empirical study aimed to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of open-source software projects. We analyze the unit test code of twenty-one open-source Java projects and detected the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from both automated unit test generators and existing unit test suites.</p>
Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative. Our results show that the subset of genes chosen for manual annotation by a research community (3.5-7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species' gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities. In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.
KNIME workflows for the evaluation of neurotoxic effects in zebrafish embryos by automatic measurement of early motor behaviours
<p>Zebrafish (<em>Danio rerio</em>) has rapidly become a popular model species for behavioural studies that may be relevant to drug screening and safety toxicology. Zebrafish embryos show a complex behavioural repertoire already a few hours after fertilization. Particularly, early stage zebrafish show characteristic behavioural features such as spontaneous tail coiling (STC) or induced movements when exposed to a short and bright light flash (called photomotor response -PMR-). In this chapter, we provide the methods for assessing STC and PMR in zebrafish embryos and to detect changes provoked by chemicals. One of the protocols uses video analysis suitable for automated high-throughput screening. Moreover, both protocols describe the use of automated video analysis by using an open-source integration platform (KNIME analytics platform), providing a flexible workflow system that can be adapted to a diversity of video recordings. We also provide a toxicological validation of this assay and show that these protocols can be used to provide an automated, high data-content readout for zebrafish behavioural responses. </p>
Our Dreams, Our Selves: Automatic Interpretation of Dream Reports
<p>Sleep scientists have shown that dreaming helps people improve their waking lives, and they have done so by developing sophisticated content analysis scales. Over the years, sleep scientists have developed hundreds of increasingly sophisticated ways of coding dreams. One of the the best validated and most widely used scale is the Hall and Van de Castle's. The Hall–Van de Castle dream coding system consists of ten categories (and their sub-categories) of elements appearing in dreams, together with detailed rules to recognize and measure those elements from written reports. The system became a standard reference for quantitative dream analysis, thanks to its objective approach that facilitates reproducibility and high inter-coder reliability. In practice, the ten categories are not all of equal importance in capturing the psycho-pathological aspects of a dream's content. Dream scientists determined that the three categories of Characters, Social Interactions and Emotions are the most valuable ones and are usually more informative than all the remaining ones combined. These are described as follows:</p> <ul> <li>Characters. People, animals and imaginary figures who appear in the dream; </li> <li>Interactions. Interactions among characters of three types: friendly, sexual and aggressive;</li> <li>Emotions. Markers of positive or negative emotions in the dream.</li> </ul> <p>Each of these three categories is quantified by a set of metrics. For example, the Characters category includes the percentages of male, animal, and imaginary characters that appears in the dream.</p> <p>As most dream content analysis scales, the Hall–Van de Castle dream coding system is complex and, as such, require human intervention. As a result, annotations have been mostly done manually, which is time-consuming, does not scale. To tackle this challenge, we designed a Natural Language Processing (NLP) tool that automatically scores dream reports by operationalizing the Hall–Van de Castle coding system. We validated the tool's effectiveness on hand-annotated dream reports and applied it to a set of 20k+ dream reports from dreambank.net. This dataset contains those algorithmic annotations.</p>
A Large Scale Study On the Effectiveness of Manual and Automatic Unit Test Generation
<p>Recently, an increasingly large amount of effort has been devoted to implementing tools to generate unit test suites automatically. Previous studies have investigated the effectiveness of these tools by comparing automatically generated test suites (ATSs) to manually written test suites (MTSs). Most of these studies report that ATSs can achieve higher code coverage, or even mutation coverage, than MTSs, particularly when suites are generated from defective code. However, these studies usually consider a limited amount of classes or subject programs, while the adoption of such tools in the industry is still low. This work aims to compare the effectiveness of ATSs and MTSs when applied as regression test suites. We conduct an empirical study, using ten programs (1368 classes), written in Java, that already have MTSs and apply two sophisticated tools that automatically generate test cases: Randoop and EvoSuite. To evaluate the test suites’ effectiveness, we use line and mutation coverage. Our results indicate that MTSs are, in general, more effective than ATSs regarding the investigated metrics. Moreover, the number of generated test cases may not indicate test suites’ effectiveness. Furthermore, there are situations when ATSs are more effective, and even when ATSs and MTSs can be complementary.</p>
An empirical study of automatically-generated tests from the perspective of test smells
<p>Developing software test code can be as or more expensive than developing software production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code quality. Nonetheless, unit tests usually do not have a quality check layer above testing code, which might be hard to guarantee the quality of the generated tests. A strategy to verify the tests quality is to analyze the presence of test smells in software test code. Test smells are characteristics in the test code that possibly indicate weaknesses in test design and implementation. Their presence in test code could be used as a quality indicator. In this paper, we present an empirical study to analyze the quality of unit test code generated by automated test tools. We compare the tests generated by two tools (Randoop and EvoSuite) with the existing unit test suite of twenty-one open-source Java projects. We analyze the unit test code to detect the presence of nineteen types of test smells. The results indicated significant differences in the unit test quality when comparing data from the automated unit test generators and existing unit test suites.</p>
Fast saccadic eye-movements in humans suggest that numerosity perception is automatic and direct.
<p><span>Fast saccades are rapid automatic oculomotor responses to salient and ecologically important visual stimuli such as animals and faces. Discriminating the number of friends, foe or prey may also have an evolutionary advantage. In this study participants were asked to saccade rapidly towards the more numerous of two arrays. Participants could discriminate numerosities with high accuracy and great speed, as fast as 190 ms. Intermediate numerosities were more likely to elicit fast saccades than very low or very high numerosities. Reaction-times for vocal responses (collected in a separate experiment) were slower, did not depend on numerical range, and correlated only with the slow, not the fast saccades, pointing to different systems. The short saccadic-reaction times we observe are surprising given that discrimination using numerosity estimation is thought to require a relatively complex neural circuit, with several relays of information through parietal and pre-frontal cortex. Our results suggest that fast numerosity-driven saccades may be generated on a single feed-forward pass of information recruiting a primitive system that cuts through the cortical hierarchy and rapidly transforms the numerosity information into a saccade command.</span></p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions: A Study of the Hibernate Developer Mailing List"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 844 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Screencast of Automatic Building of a Repository for Component-based Synthesis of Warehouse Simulation Models
<p>Screencast of the migration of an existing simulation model into a software product line.</p>
Data from: Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators
Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning system intended to provide an estimation of the LVTES anatomical region with the use of ICD-EGM in the situation where 12-lead electrocardiogram of ventricular tachycardia are not available. Several machine learning techniques were specifically designed and benchmarked, both from classification (such as Neural Networks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accuracy rates for LVTES identification in a controlled number of anatomical regions, and the regression approach quality was studied in terms of the spatial resolution. We analyzed the ICD-EGM of 23 patients (18±10 EGM per patient) during left ventricular pacing and simultaneous recording of the spatial coordinates of the pacing electrode with a navigation system. Several feature sets extracted from ICD-EGM (consisting of times and voltages) were shown to convey more discriminative information than the raw waveform. Among classifiers, the SVM performed slightly better than NN. In accordance with previous clinical works, the average spatial resolution for the LVTES was about 3 cm, as in our system, which allows it to support the faster determination of the LVTES in ablation procedures. The proposed approach also provides with a framework suitable for driving the design of improved performance future systems.
Data from: Automatic facial mimicry in response to dynamic emotional stimuli in five-month-old infants
Human adults automatically mimic others' emotional expressions, which is believed to contribute to sharing emotions with others. Although this behaviour appears fundamental to social reciprocity, little is known about its developmental process. Therefore, we examined whether infants show automatic facial mimicry in response to others' emotional expressions. Facial electromyographic activity over the corrugator supercilii (brow) and zygomaticus major (cheek) of four- to five-month-old infants was measured while they viewed dynamic clips presenting audiovisual, visual and auditory emotions. The audiovisual bimodal emotion stimuli were a display of a laughing/crying facial expression with an emotionally congruent vocalization, whereas the visual/auditory unimodal emotion stimuli displayed those emotional faces/vocalizations paired with a neutral vocalization/face, respectively. Increased activation of the corrugator supercilii muscle in response to audiovisual cries and the zygomaticus major in response to audiovisual laughter were observed between 500 and 1000 ms after stimulus onset, which clearly suggests rapid facial mimicry. By contrast, both visual and auditory unimodal emotion stimuli did not activate the infants' corresponding muscles. These results revealed that automatic facial mimicry is present as early as five months of age, when multimodal emotional information is present.
Data from: Automatic recognition of self-acknowledged limitations in clinical research literature
Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Materials and Methods: To develop our recognition methods, we used a set of 8,431 sentences from 1,197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α=0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs. 89.6%, 95% CI [88.1-91.1]). Discussion: We attribute the effectiveness of the rule-based method to the highly localized and formulaic language used in reporting of limitations in clinical research publications. Experiments with training size and composition show that more data does not necessarily lead to higher accuracy in the machine learning-based approaches. Conclusion: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.
Automatic learning of pre-miRNAs from different species – Supplemental Material
<p>In this study we investigated to which degree the features used to represent the sequences, the learning algorithm and the training and test species impacted the performance of pre-miRNA classification.</p> <p>The folder <em>multispecies.tar.xz </em>contains the data and the scripts necessary to reproduce the results in the publication in BMC Bioinformatics.</p>
A dataset for automatic vocal mode classification
<p>A dataset for automatic vocal mode classification with vocal modes as introduced by the complete vocal technique (CVT). Data was recorded at the institute for information processing and consists of around 3700 unique productions of sustained vowels. Each production was recorded by up to four microphones, yielding more than 13000 samples in total. For each sample, an annotation, created by three cvt-experienced singers, is provided. For a detailed description, read the readme.txt, which also explains precisely the naming convention and other details.</p> <p>The dataset contains a few samples that did not show a proper vocal "oscillation" in the folder "ExcludedSamples". They are not part of the actual vocal mode dataset, but were included to perhaps help other research in the future, e.g., to detect singing issues.</p>
Date Fruit Detection Dataset for Computer Vision-Based Automatic Harvesting.
<p>The "Date Fruit Detection Dataset for Computer Vision-Based Automatic Harvesting" is a collection of videos and images showcasing date fruits from four Moroccan varieties, namely Majhoul, Boufagous, Bouisthami, and Khoult. This dataset is specifically designed to detect and classify date fruits, with the primary goal being the automation of the harvesting process.</p><p>All the images in this dataset were captured in two orchards located in Morocco, with the first orchard situated in the southeast of Errachidia and the second in Tismoumine, Alnif, Tinghir. These images were taken under various natural conditions, encompassing differing lighting, contrast, shadows, and instances where the dates were concealed by bags or hidden beneath palm leaves.</p><p>The dataset was meticulously compiled over the period spanning from June to September 2022, ensuring comprehensive coverage of all four maturity stages of date fruits, which include immature, khalal, rutab, and tamer. The dataset is intended for both object detection and classification purposes, and it includes a YOLO annotation txt file for each image. These annotations have been tailored to precisely recognize not only the date fruit but also to distinguish the specific variety and its maturity stage.</p>
test data for Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis
<p>Companion python scripts are available in: https://github.com/xin-gou/automatic-ecgdiagnosis</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.