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This study aimed to provide scientific evidence and strategic suggestions for coastline management and engineering practices through analysis of the characteristics and temporal and spatial variations of wind and waves. We selected a public dataset from National Marine Data Center (NMDC) pertaining to wind and waves, subsequently performing feature selection through an analysis of the correlations among the various elements within the datasets. Furthermore, we conducted a thorough analysis and discussion of the data that was missing from these datasets, ensuring a comprehensive understanding of their limitations and potential implications. After conducting thorough statistics and analysis of the measured data pertaining to waves and winds, we studied the temporal and spatial variations observed in coastal waves. This paper provided insights into how these waves evolve and change over different time periods and locations.

[This corrects the article DOI: 10.1371/journal.pone.0201195.].

doi: 10.1038/nature19104 Corrigendum: Holocene shifts in the assembly of plant and animal communities implicate human impacts S. Kathleen Lyons, Kathryn L. Amatangelo, Anna K. Behrensmeyer, Antoine Bercovici, Jessica L. Blois, Matt Davis, William A. DiMichele, Andrew Du, Jussi T. Eronen, J. Tyler Faith, Gary R. Graves, Nathan Jud, conrad Labandeira, cindy V. Looy, brian McGill, Joshua H. Miller, David Patterson, Silvia Pineda-Munoz, Richard Potts, Brett Riddle, Rebecca Terry, Anikó Tóth, Werner Ulrich, Amelia Villaseñor, Scott Wing, Heidi Anderson, John Anderson, Donald Waller & Nicholas J. Gotelli Nature 529, 80-83 (2016); doi:10.1038/nature16447 It has come to our attention that in this Letter, there were some errors in the categorization of some of the modern datasets (R. Telford et al., personal communication).

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (Damen in Scaling egocentric vision: ECCV, 2018), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics.

The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.

SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.

The massive increase in classroom video data enables the possibility of utilizing artificial intelligence technology to automatically recognize, detect and caption students’ behaviors. This is beneficial for related research, e.g., pedagogy and educational psychology. However, the lack of a dataset specifically designed for students’ classroom behaviors may block these potential studies. This paper presents a comprehensive dataset that can be employed for recognizing, detecting, and captioning students’ behaviors in a classroom. We collected videos of 128 classes in different disciplines and in 11 classrooms. Specifically, the constructed dataset consists of a detection part, recognition part, and captioning part. The detection part includes a temporal detection data module with 4542 samples and an action detection data module with 3343 samples, whereas the recognition part contains 4276 samples and the captioning part contains 4296 samples. Moreover, the students’ behaviors are spontaneous in real classes, rendering the dataset representative and realistic. We analyze the special characteristics of the classroom scene and the technical difficulties for each module (task), which are verified by experiments. Due to the particularity of classrooms, our datasets proposes increasing the requirements of existing methods. Moreover, we provide a baseline for each task module in the dataset and make a comparison with the current mainstream datasets. The results show that our dataset is viable and reliable. Additionally, we present a thorough performance analysis of each baseline model to provide a comprehensive comparison for models using our presented dataset. The dataset and code are available to download online: https://github.com/BNU-Wu/Student-Class-Behavior-Dataset/tree/master .

The ColorChecker dataset is the most widely used dataset for evaluating and benchmarking illuminant-estimation algorithms. Although it is distributed with a 3-fold cross-validation partitioning, no procedure is defined on how to use it. In order to permit a fair comparison between illuminant-estimation algorithms, in this short correspondence we define a fair comparison procedure, showing that illuminant-estimation errors of state-of-the-art algorithms have been underestimated by up to 33%. We also compute the lower error bounds that can be reached on this dataset, which demonstrates that the existing algorithms have not yet reached their maximum performance potential.