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Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework A C lassi H onk that performs raw vehicular honk sensing, data labeling, and classifies the honk into three major groups, i.e., light-weight vehicles, medium-weight vehicles, and heavy-weight vehicles. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used and proposed an Ensembled Transfer Learning model (EnTL) for vehicle honks classification and performed comparative analysis. Results reveal that EnTL exhibits the best performance compared to pre-trained models and achieves 96.72% accuracy in our dataset. In addition, context of a location is identified based on these classified honk signatures in a city. [ABSTRACT FROM AUTHOR]

Data labeling systems are designed to facilitate the training and validation of machine learning algorithms under the umbrella of crowdsourcing practices. The current paper presents a novel approach for designing a customized data labeling system, emphasizing two key aspects: an innovative payment mechanism for users and an efficient configuration of output results. The main problem addressed is the labeling of datasets where golden items are utilized to verify user performance and assure the quality of the annotated outputs. Our proposed payment mechanism is enhanced through a modified skip-based golden-oriented function that balances user penalties and prevents spam activities. Additionally, we introduce a comprehensive reporting framework to measure aggregated results and accuracy levels, ensuring the reliability of the labeling output. Our findings indicate that the proposed solutions are pivotal in incentivizing user participation, thereby reinforcing the applicability and profitability of newly launched labeling systems. [ABSTRACT FROM AUTHOR]

Researchers often use machine learning and deep learning to predict price trends in the financial markets, aiming to achieve high returns. However, accurately predicting market prices is challenging due to their nonlinear and seemingly random nature. Improving the accuracy of the prediction model is the common focus of researchers, yet it is crucial to also consider the data used in training. Traditional labeling methods used in most price trend prediction studies are not robust as they are sensitive to small price changes, leading to inefficient model training. To address this issue, this study introduces a Dynamic Threshold Breakout (DTB) labeling system that labels data based on the price percentage change during a specific period. This proposed labeling system was then integrated into an automated trading system using LightGBM and evaluated using three different markets. The results showed that the DTB labeling method is effective for trading in financial markets in terms of winning ratio, payoff ratio, profit factor, accuracy and ROI in trading performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research, we introduce a novel open-source labeling tool, the Game-Based Assessment (GBA) Labeling Tool, specifically designed to address current challenges for data labeling in GBA scenarios. This web-based application facilitates the annotation of audio, video, and game event data, offering three different types of annotations – global, time instant, and time window annotations – to enhance accuracy in the labeling process. The tool also offers customizable labels and various types of visualizations to support different contexts and scenarios.

Purpose: To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images. Design: Evaluation of artificial intelligence (AI) algorithms. Subjects: The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing. Methods: Training data consisted of 2 sets of FAF images; 1 with area measurements only and no indication of GA location (Weakly labeled) and the second with GA segmentation masks (Strongly labeled). Main Outcome Measures: Bland–Altman plots and scatter plots were used to compare GA area measurement between ground truth and AI measurements. The Dice coefficient was used to compare accuracy of segmentation of the Strong model. Results: In the cross-validation AREDS2 data set (n = 601), the mean (standard deviation [SD]) area of GA measured by human grader, Weakly labeled AI model, and Strongly labeled AI model was 6.65 (6.3) mm2, 6.83 (6.29) mm2, and 6.58 (6.24) mm2, respectively. The mean difference between ground truth and AI was 0.18 mm2 (95% confidence interval, [CI], −7.57 to 7.92) for the Weakly labeled model and −0.07 mm2 (95% CI, −1.61 to 1.47) for the Strongly labeled model. With GlaxoSmithKline testing data (n = 156), the mean (SD) GA area was 9.79 (5.6) mm2, 8.82 (4.61) mm2, and 9.55 (5.66) mm2 for human grader, Strongly labeled AI model, and Weakly labeled AI model, respectively. The mean difference between ground truth and AI for the 2 models was −0.97 mm2 (95% CI, −4.36 to 2.41) and −0.24 mm2 (95% CI, −4.98 to 4.49), respectively. The Dice coefficient was 0.99 for intergrader agreement, 0.89 for the cross-validation data, and 0.92 for the testing data. Conclusions: Deep learning models can achieve reasonable accuracy even with Weakly labeled data. Training methods that integrate large volumes of Weakly labeled images with small number of Strongly labeled images offer a promising solution to overcome the burden of cost and time for data labeling. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Research into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection. For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels. Our study illustrates that different labeling methodologies directly impact the annotations' quality, as well as the capabilities of a deep learning classifier trained with the data. We noticed that in situ methods produce less but more precise labels than recall methods. Furthermore, we combined an activity diary with a visualization tool that enables the participant to inspect and label their activity data. Due to the introduction of such a tool were able to decrease missing annotations and increase the annotation consistency, and therefore the F1-Score of the deep learning model by up to 8% (ranging between 82.1 and 90.4% F1-Score). Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they could introduce, and the consequences of their usage on human activity recognition studies as well as possible solutions.

Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required labeling process is resource-intensive. We propose an improved change detection method based on segmentation to address this challenge. First, change detection annotations are converted to incomplete segmentation annotations through label matching. During segmentation, we utilize the focused information-guided segmentation method (FIGS) and a greenness index to provide prior information during training, guiding the model using accurately labeled regions. Finally, we generate a change map using pretrained features obtained from the segmentation stage. We demonstrate the robustness of our proposed label-matching process by comparing the results to a correctly matched dataset and show that incorporating FIGS and the greenness index improves the segmentation performance. Our method achieves effective change detection results even in scenarios associated with a shortage of annotations.

The integrity of data is crucial for the majority of existing data analysis methods. However, incomplete and unbalanced datasets from the collection and organization process will affect analysis accuracy. Existing interpolation algorithms often overlook feature importance, resulting in either cumbersome processes or underutilization of data information. This study introduces an interpolation method based on clustering algorithm focused on improving the accuracy and efficiency of missing data processing in datasets. First, in this paper, we clarify the problem to be solved about data interpolation, and we consider the importance of the information brought by the data itself to the interpolation, so we propose a scheme that combines clustering and interpolation. We propose a new method that uses the Lp norm as a similarity measure in the K-means clustering algorithm, and introduce a controllable weighting formula based on the current data segmentation. Methodologically, the clustering and interpolation are synchronized by iteratively updating the variable optimization cost function. The experimental results demonstrate significant improvements of the proposed interpolation algorithm over traditional techniques, particularly in tasks such as data labeling and classification within real datasets for clustering and classification.

Data labeling systems are designed to facilitate the training and validation of machine learning algorithms under the umbrella of crowdsourcing practices. The current paper presents a novel approach for designing a customized data labeling system, emphasizing two key aspects: an innovative payment mechanism for users and an efficient configuration of output results. The main problem addressed is the labeling of datasets where golden items are utilized to verify user performance and assure the quality of the annotated outputs. Our proposed payment mechanism is enhanced through a modified skip-based golden-oriented function that balances user penalties and prevents spam activities. Additionally, we introduce a comprehensive reporting framework to measure aggregated results and accuracy levels, ensuring the reliability of the labeling output. Our findings indicate that the proposed solutions are pivotal in incentivizing user participation, thereby reinforcing the applicability and profitability of newly launched labeling systems.

ABSTRACTBig Earth Data refers to the multidimensional integration and association of scientific data, including geography, resources, environment, ecology, and biology. An effective data classification system and label management strategy are important foundations for long-term management of data resources. The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program (CASEarth). This study constructed two sets of classification and coding systems that realize classification by mapping each other; namely, the geosphere-level and Sustainable Development Goals (SDGs) indicator classifications. This technique was based on natural language processing technology and solved problems with subject-word segmentation, weight calculation, and dynamic matching. A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100. Furthermore, we expect our study to provide the methodology and technical support for user-oriented classification and label management services for Big Earth Data.

The continuous changes of the external environment lead to the performance regression of neural networksbased on traditional deep learning methods.Therefore,continual learning(CL) area gradually attracts the attention of more researchers.For edge intelligence,the CL model not only needs to overcome catastrophic forgetting,but also needs to face the huge challenge of severely limited resources.This challenge is mainly reflected in the lack of labeled resources and powerful devices.However,the existing classic CL methods usually rely on a large number of labeled samples to maintain the plasticity and stability,and the lack of labeled resources will lead to a significant accuracy drop.Meanwhile,in order to deal with the problem of insufficient annotation resources,semi-supervised learning methods often need to pay a large computational and memory overhead for higher accuracy.In response to these problems,a low-cost semi-supervised CL method named edge hierarchicalmemory learner (EdgeHML) is proposed.EdgeHML can effectively utilize a large number of unlabeled samples and a small number of labeled samples.It is based on a hierarchical memory pool,leverage multi-level storage structure to store and replay samples.EdgeHML implements the interaction between different levels through a combination of online and offline strategies.In addition,in order to further reduce the computational overhead for unlabeled samples,EdgeHML leverages a progressive learning method.It reduces the computation cycles of unlabeled samples by controlling the learning process.Experimental results show that on three semi-supervised CL tasks,EdgeHML can improve the model accuracy by up to 16.35% compared with the classic CL method,and the training iterations time can be reduced by more than 50% compared with semi-supervised methods.EdgeHML achieves a semi-supervised CL process with high performance and low overhead for edge intelligence.

The fast expansion of the Internet of Things (IoT) networks raises the possibility of further network threats. In today's world, network traffic analysis has become an increasingly critical and useful tool for monitoring network traffic in general and analyzing attack patterns in particular. A few years ago, distributed denial-of-service attacks on IoT networks were considered the most pressing problem that needed to be addressed. The absence of high-quality datasets is one of the main obstacles to applying DDOS detection systems based on machine learning. Researchers have developed numerous methods to extract and analyze information from recorded files. From a literature review, it is clear that most of these tools share similar drawbacks. In this study, we proposed an intelligent raw network data extractor and labeler tool by incorporating the limitations of the tools that are available to transform PCAP to CSV. To generate and process a high-quality DDOS attack dataset suitable for machine learning models, we employed several data preprocessing operations on the selected network intrusion dataset. To confirm the validity and acceptability of the dataset, we tested different models. Among the models tested, the random forest was the most accurate in detecting the DDOS attack. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human–robot collaboration has emerged as a prominent research topic in recent years. To enhance collaboration and ensure safety between humans and robots, researchers employ a variety of methods. One such method is physiological computing, which aims to estimate a human's psycho-physiological state by measuring various physiological signals such as galvanic skin response (GSR), electrocardiograph (ECG), heart rate variability (HRV), and electroencephalogram (EEG). This information is then used to provide feedback to the robot. In this paper, we present the latest state-of-the-art methods in physiological computing for human–robot collaboration. Our goal is to provide a comprehensive guide for new researchers to understand the commonly used physiological signals, data collection methods, and data labeling techniques. Additionally, we have categorized and tabulated relevant research to further aid in understanding this area of study. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-PC. All these procedures are merged in the proposed framework and evaluated in one industrial Use-Case with flexible patch sizes. Comparing the new approach with standard methods has shown that the annotation process can be accelerated by 5.2 times through automation.

A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances. REGAL interactively creates high-quality labeling patterns from raw text, enabling a single annotator to accurately label an entire dataset after initialization with three keywords for each class. Experiments demonstrate that REGAL extracts up to 3 times as many high-accuracy labeling functions from text as current state-of-the-art methods for interactive weak supervision, enabling REGAL to dramatically reduce the annotation burden of writing labeling functions for weak supervision. Statistical analysis reveals REGAL performs equal or significantly better than interactive weak supervision for five of six commonly used natural language processing (NLP) baseline datasets.

Investing in the stock market involves high risks and equivalent returns. Many investors tend to be loss averse and suffer significant losses due to information asymmetry and emotion-based investing. In order to minimize such losses, various studies are attempting to predict the stock market's future and automate investment decisions. However, the stock market is affected by many factors and poses a challenge in prediction. Also, many researchers who develop stock trading systems are interested primarily in improving prediction model performance. Particularly, most studies set up-down labels in the data labeling phase. However, up-down labeling views all stock price fluctuations as identical, resulting in inefficient model training by including substantial noise in the learning data. Therefore, this study proposes SGA (Selective Genetic Algorithm) labeling to overcome such concerns. A trading system was developed using the proposed method, and a machine learning model and ensemble method supported the label prediction phase. The proposed trading system was analyzed empirically with 38 NASDAQ stocks and 33 KOSPI stocks, and a comparative experiment with up-down labeling method was conducted. In this study, SGA labeling performance exceeded that of up-down labeling. Additionally, instance selection and ensemble methods affected the trading system's performance. [ABSTRACT FROM AUTHOR]

Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework A C lassi H onk that performs raw vehicular honk sensing, data labeling, and classifies the honk into three major groups, i.e., light-weight vehicles, medium-weight vehicles, and heavy-weight vehicles. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used and proposed an Ensembled Transfer Learning model (EnTL) for vehicle honks classification and performed comparative analysis. Results reveal that EnTL exhibits the best performance compared to pre-trained models and achieves 96.72% accuracy in our dataset. In addition, context of a location is identified based on these classified honk signatures in a city., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Human–robot collaboration has emerged as a prominent research topic in recent years. To enhance collaboration and ensure safety between humans and robots, researchers employ a variety of methods. One such method is physiological computing, which aims to estimate a human’s psycho-physiological state by measuring various physiological signals such as galvanic skin response (GSR), electrocardiograph (ECG), heart rate variability (HRV), and electroencephalogram (EEG). This information is then used to provide feedback to the robot. In this paper, we present the latest state-of-the-art methods in physiological computing for human–robot collaboration. Our goal is to provide a comprehensive guide for new researchers to understand the commonly used physiological signals, data collection methods, and data labeling techniques. Additionally, we have categorized and tabulated relevant research to further aid in understanding this area of study.

The main ionospheric trough (MIT) is a salient density feature in the mid‐latitude ionosphere and characterizing its structure is important for understanding Global Positioning System and HF signal propagation, and identifying geospace phenomena such as the plasmapause boundary layer. While a number of previous studies have statistically investigated the properties of the MIT utilizing low‐altitude satellite observations, they have been limited to latitudinal cross sections, and have not considered the inherent two‐dimensional structure of the MIT. In this work, we develop a regularized inversion method for identifying the two dimensional structure of the MIT in Total Electron Content maps. Because no ground truth labels exist for the MIT, we extensively characterize the behavior of the algorithm by comparing it to the method developed by Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583). We show that statistics computed on the resulting labels are robust to our choice of algorithm parameters and that we are able to match the results of Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583) with a particular selection of the parameters. In addition to enabling fundamentally different studies, our MIT labels are able to provide statistical MIT properties with higher resolution. Code to reproduce our data set is provided in a GitHub repository: https://github.com/gregstarr/trough. Plain Language Summary: The Earth's ionized outer atmosphere (the ionosphere) serves as the interface between our atmosphere and the near space environment. Signals from Global Positioning System satellites contain information about the total integrated plasma density (referred to as "Total Electron Content," (TEC)) between a given satellite and a given ground receiver. Measurements of TEC from large networks of ground receivers allow us to construct global images of the ionosphere, and thus study its variations in space and time. This paper uses such ionospheric images over a 20 year period to investigate a large‐scale coherent density depletion in the ionosphere known as the "mid‐latitude ionospheric trough" (MIT). In this work we have identified and labeled MIT regions in each images using computer vision techniques, and stored the results in a new image database. Using this "labeled data," we can gain insight into the average location, morphology, and occurrence rates of the MIT, an examine this phenomenon varies with season, time of day, geomagnetic activity, and other correlated parameters. Key Points: A method for identifying the main ionospheric trough (MIT) in global Total Electron Content images is developed and validatedStatistics extracted from a 10‐year data set are found to be in good agreement with a SWARM‐derived satellite modelOur technique enables statistical analyzes of two‐dimensional (lat, lon) MIT properties with high resolution [ABSTRACT FROM AUTHOR]

The advent of machine learning (ML) has significantly advanced the recognition and translation of sign languages, bridging communication gaps for hearing-impaired communities. At the heart of these technologies is data labeling, crucial for training ML algorithms on a huge amount of consistently labeled data to achieve models that generalize well. The adoption of language-agnostic annotations is essential to connect different sign languages, as single-language databases often provide limited lexicon examples, insufficient for training robust ML algorithms. This study critically examines the Hamburg Sign Language Notation System (HamNoSys), which describes the signer's initial position and body movements, in contrary to the meanings of glosses. Despite HamNoSys's utility in standardizing transcriptions across various sign languages, our investigation uncovers inconsistencies within HamNoSys that may negatively impact the development of accurate and reliable ML models. By analyzing HamNoSys labels across five sign languages, we identified a lack of standardized annotation procedures and the complexities within HamNoSys that introduce biases and errors. Our findings underscore the urgent need for unified, standardized data annotation guidelines to enhance the accuracy and efficiency of sign language recognition technologies. This research highlights the importance of addressing annotation challenges and advocates for a comprehensive, diversified database to improve the generalization of ML models. • HamNoSys notation system is not sufficient for ML labeling purposes • HamNoSys-based labels are often subjective and inconsistent • There is a need for a notation system useful in computer-aided systems [ABSTRACT FROM AUTHOR]

A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances. REGAL interactively creates high-quality labeling patterns from raw text, enabling a single annotator to accurately label an entire dataset after initialization with three keywords for each class. Experiments demonstrate that REGAL extracts up to 3 times as many high-accuracy labeling functions from text as current state-of-the-art methods for interactive weak supervision, enabling REGAL to dramatically reduce the annotation burden of writing labeling functions for weak supervision. Statistical analysis reveals REGAL performs equal or significantly better than interactive weak supervision for five of six commonly used natural language processing (NLP) baseline datasets. [ABSTRACT FROM AUTHOR]

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time‐consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed‐pixels, ‐textures, and ‐spectral signatures, can be especially error‐prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi‐automated, method for interactive segmentation of N‐dimensional (x, y, N) images into two‐dimensional (x, y) label images. It uses human‐in‐the‐loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non‐photographic imagery consist of 1‐ and 3‐bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation. Plain Language Summary: Labeling pixels in scientific images by hand is time‐consuming and error‐prone, so we would like to train computers to do that for us. We can use automated techniques from Artificial Intelligence or AI, like one called Deep Learning, but it needs a lot of example images and corresponding labels that have been made by hand. So, we still need to label quite a lot of images at the pixel level—called image segmentation. We made a computer program called Doodler that speeds up the process; you label some pixels, and it labels the rest. It is the fastest method we know of for image segmentation because it is semi‐automated. We also show that it produces accurate and precise labeling, as we demonstrated by having multiple people use this method to label the same images. Because it is so fast and accurate, it allows us to get enough data to train Deep Learning models to do segmentation on all the images we have, from the past and in the future. Doodler therefore enables geoscientists to use Artificial Intelligence to extract much more information from their imagery, in service of geoscience in general. Key Points: Methodology for image segmentation based on agreement between a labeler and a machine learning modelFaster and more accurate segmentation of interpretable imagery compared to traditional labelingLarge multi‐labeler consensus facilitates reproducible scientific inference from Earth surface images [ABSTRACT FROM AUTHOR]

An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation. A new unsupervised Adversarial Clustering System (ACS) is proposed. The ACS is an adversarial learning approach comprising an unsupervised clustering module generating machine labels and a supervised classification module classifying the data based on the machine labels. Both modules are linked through an optimization algorithm iteratively improving the unsupervised clusters. The objective function driving the improvement consists of the within-cluster sum of squares (WCSS) error and the supervised classification accuracy. The proposed method was tested on three different fine-art datasets, including two sets of paintings previously categorized by art experts and one never categorized collection of Australian Aboriginal paintings. The unsupervised clusters were analyzed using standard unsupervised clustering metrics and a reliability measure between machine and human labeling. The ACS showed higher reliability compared to the classical k-means clustering method. The content analysis of unsupervised clusters indicated grouping based on scene composition, type, and shape of the object, edge sharpness and direction, and color palette.