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Integrating spatial inequality perspectives in strategic decision-making can ensure positive impacts on resource distribution for public welfare and sustainable development. This study aims to apply evidence-based approaches in deploying permanent deacons. The empirical case study has been conducted at the St Helens denary of the Liverpool archdiocese, UK. Assisting with charitable works is one of three served areas by the Roman Catholic Church facilitated by deacons. The deployment of permanent deacons could benefit from being evidence-based so that a deacon can serve to ease the socio-economic (e.g., population density, long-term health conditions, housing system, employment status, education level, social status) inequality in the most deprived area. We used geographic information system (GIS) based algorithms, Getis-Ord Gi* for hot spot analysis to find the clustered area by considering the socio-economic factors. The open/freely available government census dataset was found to help extract socio-economic parameters. Furthermore, a GIS-based multi-criteria assessment technique was conducted by applying map algebra (raster calculator) to identify the deprived area with ranks considering multiple socio-economic conditions, where served areas by the existing deacons were considered to constrain. The served areas were estimated by applying network analysis where OpenStreetMap and location existing deacons were used as input. Our empirical case study identified the central and northern parts of the deanery as the most and least deprived areas, respectively. Finally, Liverpool archdiocese could consider deploying new permanent deacons in St Helens denary based on suggested deprivation ranks. Therefore, the appropriate number of deacons in the deprived areas can quickly and effectively respond to the needy and enhance communities' resilience and sustainable development by ensuring proportionate resource distribution. [ABSTRACT FROM AUTHOR]

Online experiments have been transforming the field of behavioral research, enabling researchers to increase sample sizes, access diverse populations, lower the costs of data collection, and promote reproducibility. The field of developmental psychology increasingly exploits such online testing approaches. Since infants cannot give explicit behavioral responses, one key outcome measure is infants' gaze behavior. In the absence of automated eyetrackers in participants' homes, automatic gaze classification from webcam data would make it possible to avoid painstaking manual coding. However, the lack of a controlled experimental environment may lead to various noise factors impeding automatic face detection or gaze classification. We created an adult webcam dataset that systematically reproduced noise factors from infant webcam studies which might affect automated gaze coding accuracy. We varied participants' left-right offset, distance to the camera, facial rotation, and the direction of the lighting source. Running two state-of-the-art classification algorithms (iCatcher+ and OWLET) revealed that facial detection performance was particularly affected by the lighting source, while gaze coding accuracy was consistently affected by the distance to the camera and lighting source. Morphing participants' faces to be unidentifiable did not generally affect the results, suggesting facial anonymization could be used when making online video data publicly available, for purposes of further study and transparency. Our findings will guide improving study design for infant and adult participants during online experiments. Moreover, training algorithms using our dataset will allow researchers to improve robustness and allow developmental psychologists to leverage online testing more efficiently. [ABSTRACT FROM AUTHOR]

Abstract We present an open access dataset for development, evaluation, and comparison of algorithms for individual tree detection in dense mixed forests. The dataset consists of a detailed field inventory and overlapping UAV LiDAR and RGB orthophoto, which make it possible to develop algorithms that fuse multimodal data to improve detection results. Along with the dataset, we describe and implement a basic local maxima filtering baseline and an algorithm for automatically matching detection results to the ground truth trees for detection algorithm evaluation.

We present an open access dataset for development, evaluation, and comparison of algorithms for individual tree detection in dense mixed forests. The dataset consists of a detailed field inventory and overlapping UAV LiDAR and RGB orthophoto, which make it possible to develop algorithms that fuse multimodal data to improve detection results. Along with the dataset, we describe and implement a basic local maxima filtering baseline and an algorithm for automatically matching detection results to the ground truth trees for detection algorithm evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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.)

Integrating spatial inequality perspectives in strategic decision-making can ensure positive impacts on resource distribution for public welfare and sustainable development. This study aims to apply evidence-based approaches in deploying permanent deacons. The empirical case study has been conducted at the St Helens denary of the Liverpool archdiocese, UK. Assisting with charitable works is one of three served areas by the Roman Catholic Church facilitated by deacons. The deployment of permanent deacons could benefit from being evidence-based so that a deacon can serve to ease the socio-economic (e.g., population density, long-term health conditions, housing system, employment status, education level, social status) inequality in the most deprived area. We used geographic information system (GIS) based algorithms, Getis-Ord Gi* for hot spot analysis to find the clustered area by considering the socio-economic factors. The open/freely available government census dataset was found to help extract socio-economic parameters. Furthermore, a GIS-based multi-criteria assessment technique was conducted by applying map algebra (raster calculator) to identify the deprived area with ranks considering multiple socio-economic conditions, where served areas by the existing deacons were considered to constrain. The served areas were estimated by applying network analysis where OpenStreetMap and location existing deacons were used as input. Our empirical case study identified the central and northern parts of the deanery as the most and least deprived areas, respectively. Finally, Liverpool archdiocese could consider deploying new permanent deacons in St Helens denary based on suggested deprivation ranks. Therefore, the appropriate number of deacons in the deprived areas can quickly and effectively respond to the needy and enhance communities’ resilience and sustainable development by ensuring proportionate resource distribution.

Data have become valuable assets for enterprises. Data governance aims to manage and reuse data assets, facilitating enterprise management and enabling product innovations. A data lineage graph (DLG) is an abstracted collection of data assets and their data lineages in data governance. Analyzing DLGs can provide rich data insights for data governance. However, the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs. This paper introduces an open dataset of DLGs, including the DLG model, the dataset construction process, and applied areas. This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited, which contains 18 DLGs with three types of data assets and two types of relations. To the best of our knowledge, this dataset is the first open dataset of DLGs for data governance. This dataset can also support the development of other application areas, such as graph analytics and visualization.

Document image rectification problem is crucial in document analysis. Most of the current state-of-the-art methods addressing it are data-driven and rely on neural network approaches. However, despite satisfactory rectifications, such methods’ time performance is poor, making them unsuitable for mobile on-device acquisition. The present work concentrates on a specific (but common) case of document physical distortion – the documents with a single crease. We investigate the properties of a surface comprised of two planes captured by a pinhole camera. Namely, we provide the methods to obtain the transformation between such an image and the template image having successfully localized the document in a frame. It can be utilized in on-device recognition systems: it takes only 3 ms to estimate transformation parameters and about a quarter of a second to rectify an image on a smartphone CPU. We propose a novel dataset FDI-AC containing 200 real images of documents with a single crease in different positions. We conduct experiments comparing our approach with the current state-of-the-art setting a baseline performance on FDI-AC. These experiments show that the proposed algorithm outperforms image rectification transformer network GeoTr in rectification accuracy and time performance.

This paper is focused on the field of computer vision in order to investigate the presentation properties of retinal blood vessels. Combining the structure of convolutional neural networks, activation functions, and common metrics in semantic segmentation, a dynamic network model for retinal vessel segmentation based on computer vision is constructed. The purpose of this paper is to discuss the results of retinal vascular segmentation based on computer vision. The image connection and alignment pattern selection process is also established to match retinal vessel images by computer vision. The performance of the dynamic network constructed here and the results of retinal vessel segmentation were then analyzed in three publicly available datasets, DRIVE (digital retinal images for vessel extraction), CHASE_DB1, and STARE (structured snalysis of the retinal. The ROC (retinopathy online challenge) curves on all three datasets exceeded 0.9, showing high performance, and the area under the PR curve exceeded 0.88. The accuracy of the results for retinal vessel segmentation was around 96%. Based on the semantic segmentation direction in the field of computer vision in this study, the dynamic network for retinal vessel segmentation can be well constructed. [ABSTRACT FROM AUTHOR]

Inverse problems involve extracting information from indirect measurements. Many of these problems are ill-posed, making the recovery process unstable and sensitive to errors and noise. Specially designed algorithms are essential for robust solutions in such cases. Computed tomography (CT), which aims to determine an object's interior structure from X-ray projections, is a widely used application that requires solving an ill-posed problem. In this work we present the Helsinki Tomography Challenge 2022 (HTC2022), aimed to foster algorithm development in the field of CT reconstruction. HTC2022 focused specifically on limited-angle tomography to produce segmented reconstructions of disc-shaped imaging phantoms with holes of varying complexity and with progressively reduced angular range in seven levels of difficulty. This work also presents the dataset used in the competition, now publicly available. Real data is crucial for testing algorithms against the complexities of real-world scenarios, and this dataset can now be used by the reconstruction algorithm development community. HTC2022 was a global competition, with nine teams from seven countries participating and submitting a total of 22 algorithms. The competition results indicate interesting solutions in limited-angle tomography, with high-quality reconstructions that demonstrated promising directions for future research. [ABSTRACT FROM AUTHOR]

This paper considers an issue of delineating agricultural fields in satellite images. In this task we follow a multi-temporal data approach. We show that on such data, good quality can be achieved using a simple low-parameter method. The method consists of a combination of a field detector and an edge detector. The field detection is based on an Otsu thresholding technique and for the edge detection we use a Canny detector. Facing a lack of available datasets and aiming to estimate the proposed algorithm, we prepared and published our dataset consisting of 18,859 expertly annotated fields using Sentinel-2 data. We implement one of the state-of-the-art deep-learning approaches and compare it with the proposed method on our dataset. The experiment shows the proposed simple multi-temporal algorithm to outperform the state-of-the-art instant data approach. This result confirms the importance of using multi-temporal data and for the first time demonstrates that the delineation problem can be solved at a lower cost without loss of quality. Our approach requires a significantly less amount of training data when compared with the NN-based one. The dataset of agricultural fields used in the work and the proposed algorithm implementation in Python are published in open access.

Abstract Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.

This dataset constitutes a comprehensive inventory of 125 excavated sites from the territory of southern Bilad al-Sham attesting the occurrence of the Islamic Cream Ware (ICW). It provides information on the typological variety, dating, and general contexts of appearance of this pottery class. In addition, it is supplemented by bibliographical references to all sites included in the database. In general, the following dataset can appear useful for scholars working on the various subjects related to Early and Middle Islamic pottery and settlement of southern Bilad al-Sham.

This data article is based on the International Social Survey Programme (ISSP) Environment III Dataset and linked to a publication in Ecological Economics, in which we developed a model for explaining and predicting sustainable consumption behavior of Europeans using data of nine participating countries. In our study, we find that sustainable consumption behavior can be associated with environmental concern, which is influenced by increased levels of environmental knowledge and environmental risk perception. In this companion data article, we describe the usefulness, value, and relevance of the open ISSP dataset and take our linked article as an example. The data are publicly available via the GESIS-website (gesis.org). The dataset consists of individual-based interviews on the respondents’ perceptions regarding a variety of social matters, such as the environment, which we argue is especially suitable for PLS-SEM applications (for example, to conduct cross-sectional analyses).

Clinical decision support based on artificial intelligence (AI) methods has increasingly been employed in medical applications to support medical diagnosis. Developing efficient AI methods, however, depends necessarily on the availability of sufficiently large amount of data to provide reliable results. But, in medicine, it is not always possible to find sufficient amount of real data on all pathologies, particularly, for rare diseases. This paper proposes a methodological framework for generating synthetic data using data augmentation techniques combined with epidemiological profiles. It focuses on Uveitis, a rare disease in ophthalmology, which is difficult to diagnose because of the disparity in prevalence of its etiologies. The generated synthetic data have been qualitatively validated by specialist ophthalmologists and quantitatively tested using machine learning methods. Results show that, of a randomly selected sample of the generated data, more than 55% were assessed as good or excellent, which is very promising for generating synthetic, validated as near-real, medical data for rare diseases. They also show that the proposed framework is consistent in generating synthetic data, for Uveitis pathology, of different dataset sizes, achieving more than 80% diagnosis prediction accuracy for 2000 patient records or larger.

Abstract In this paper, a novel deep learning dataset, called Air2Land, is presented for advancing the state‐of‐the‐art object detection and pose estimation in the context of one fixed‐wing unmanned aerial vehicle autolanding scenarios. It bridges vision and control for ground‐based vision guidance systems having the multi‐modal data obtained by diverse sensors and pushes forward the development of computer vision and autopilot algorithms targeted at visually assisted landing of one fixed‐wing vehicle. The dataset is composed of sequential stereo images and synchronised sensor data, in terms of the flying vehicle pose and Pan‐Tilt Unit angles, simulated in various climate conditions and landing scenarios. Since real‐world automated landing data is very limited, the proposed dataset provides the necessary foundation for vision‐based tasks such as flying vehicle detection, key point localisation, pose estimation etc. Hereafter, in addition to providing plentiful and scene‐rich data, the developed dataset covers high‐risk scenarios that are hardly accessible in reality. The dataset is also open and available at https://github.com/micros‐uav/micros_air2land as well. The cover image is based on the Research Article Air2Land: A deep learning dataset for unmanned aerial vehicle autolanding from air to land by Tianjiang Hu et al., https://doi.org/10.1049/csy2.12045.

Dams are important water conservation hubs; however, dam locations provided by open datasets are often unreliable. The aim of this article is to provide a single, geographic location-reliable dataset of dams for the scientific community by fusing existing dam datasets and verifying these locations. Using Southeast Asia as the case study, we propose an efficient and automatic method to verify dam locations and developed a process framework. First, the possible location of a dam was obtained by analyzing its geographic location characteristics. Then, the deep learning method was used to detect dams. Finally, a variety of geographic knowledge was applied to comprehensively verify the detection results to obtain accurate and reliable dam location information. The fused dam dataset we produced includes the locations of 4493 dams in Southeast Asia, which were verified using the proposed framework. The verification results were then evaluated via manual visual inspection. The verification accuracy of the framework was 86.7%. The experimental results show that the proposed framework can quickly and reliably verify dam spatial locations and provide solutions for the spatial location verification of other remote sensing objects.

Event classification with machine learning methods, has now become the research spotlight for phase sensitive optical time domain reflectometer (Ф-OTDR, also referred to as DAS or DVS) system. Unlike in the computer science field, however, each team works on its own dataset and their codes in private, which makes it difficult to compare and evaluate different models on a fair basis and impairs the value of those work. Hence, we set up an intensity-based Ф-OTDR system, from which we collect data, build and make public the first open dataset, according to our searches via google. It contains six types of events with a total of 15,612 samples. We also publicize codes for two common baseline models, which are the SVM (support vector machine, 1D method) and CNN (convolutional neural network, 2D approach) models. The average recognition rates are greater than 82 % for SVM and 94 % for CNN. The dataset and codes can be used as data source and baselines for Ф-OTDR event classification, providing fair evaluation criteria and for further development of machine learning models.

Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can't meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction. [ABSTRACT FROM AUTHOR]

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers. [ABSTRACT FROM AUTHOR]

The present work deals with the reconstruction of the flood wave that hit Mandra town (Athens, Greece) on 15 November 2017, using the framework of forensic hydrology. The flash flood event was caused by a huge storm event with a high level of spatial and temporal variability, which was part of the Medicane Numa-Zenon. The reconstruction included: (a) the post-event collection of 44 maximum water depth traces in the town; and (b) the hydrodynamic simulation employing the HEC-RAS and MIKE FLOOD software. The derived open dataset (which also includes additional data required for hydrodynamic modeling) is shared with the community for possible use as a benchmark case for flood model developers. With regards to the modeling issues, we investigate the calibration strategies in computationally demanding cases, and test whether the calibrated parameters can be blindly transferred to another simulator (informed modeling). Regarding the calibration, it seems that the coupling of an initial screening phase with a simple grid-search algorithm is efficient. On the other hand, the informed modeling concept does not work for our study area: every numerical model has its own dynamics while the parameters are of grey-box nature. As a result, the modeler should always be skeptical about their global use. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Defense Sciences / Savunma Bilmleri Dergisi is the property of Turkish Military Academy Defense Sciences Institute 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.)

Background: Type 1 diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise, and hormones impact insulin needs. Beyond carbohydrates, most factors remain underexplored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems are little researched; such systems can provide data-driven insights to improve the understanding and treatment of T1D., Objective: The aim is to discover unexpected temporal patterns in insulin needs and to analyze how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose or where increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs., Methods: We analyzed time series data on insulin on board (IOB), carbohydrates on board (COB), and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via k-means clustering), weekdays, and months were determined by comparing the 95% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB, and IG across temporal dichotomies were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analyzed using Granger causality., Results: On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3

This commentary explores features of the "Saraga" article and open dataset, discussing some of the issues arising. I argue that the CompMusic project and this resulting dataset are impressive for their sensitivity to cultural specificities of the Hindustani and Carnatic musical styles; for example, the dataset includes manual annotations based on music theoretical concepts from within the styles, rather than imposing conceptual categories from outside. However, I propose there are aspects of the dataset's manual annotations that require clarification in order for them to be used as ground truths by other researchers. In addition, I raise questions regarding the representativeness of the dataset – an issue that has ethical implications.

Telephone scams, with their profound psychological impact, often compel victims to make hasty and severe decisions. Studying these scams is challenging due to the scarcity of comprehensive datasets, a result of the private nature of telephone interactions. In this paper, we introduce ScamGen , a template-based data augmentation technique designed to enhance Chinese telephone scam data. ScamGen leverages psychological insights to generate diverse and realistic scam scenarios, focusing on the psychological dynamics between scammers and victims. This novel approach integrates psychological theory with data augmentation, diverging from traditional methods by emphasizing scammer–victim interactions. Our method begins with a multi-source data collection framework, compiling an initial seed dataset of tele-scam samples. Using sentence- and word-level perturbations, we expand this seed data to create a comprehensive and diverse dataset covering a wide range of scam scenarios. Rigorous evaluations demonstrate that ScamGen outperforms large language models in generating high-quality, varied datasets. Additionally, we develop five deep learning models for intent detection on this dataset, with BERT achieving the highest precision at 86.68%. The dataset, which will be made publicly available, marks a significant step toward understanding scammer tactics and improving tele-scam detection systems. • Introduces ScamGen , a technique for enhancing Chinese telephone scam data. • Leverages psychological insights to generate diverse scam scenarios. • Expands a seed dataset using sentence- and word-level perturbations. • Outperforms large language models in quality and variety of datasets. • Develops deep learning models for intent detection, with BERT achieving 86.68%. [ABSTRACT FROM AUTHOR]

Crowd flow prediction has been a significant problem in smart cities. Real‐world datasets are the basis of designing data‐driven models, for example, machine learning and deep learning models. However, some well‐known datasets, for example, TaxiBJ, become unavailable when the original sharing links are deleted as the data providers move to new jobs. As an alternative choice, TaxiBJ21 is proposed in this letter for future crowd flow prediction studies. This new open dataset contains the taxi inflow and outflow matrices in Beijing for 3 months, with the same data format as TaxiBJ and can be used in the current literature seamlessly. In this letter, the process of constructing TaxiBJ21 is introduced and three baselines including two simple historical models and a multilayer perceptron neural network are evaluated on this new dataset as benchmarks. Both the data and baselines are publicly available in a Github repository. [ABSTRACT FROM AUTHOR]

Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset-A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional k NN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: https://github.com/Demozsj/Detection-Sperm. [ABSTRACT FROM AUTHOR]

The dataset contains the first-ever comprehensive biocodicological analysis of medieval library books and charters using Zooarchaeological Mass Spectrometry (ZooMS). Here, we analyze 68 codices and 59 charters (1490+59 samples in total) from one single monastic institution, namely the Cistercian abbey of Orval in present-day Belgium. The data entails both peptide mass fingerprinting (using MALDI-ToF) and peptide sequencing (using LC-MS/MS) analysis of almost the entire library and all the preserved single leaf charters from the monastery. MALDI-ToF data is stored in Zenodo – a multidisciplinary open access repository while the LC-MS/MS data is deposited in ProteomeXchange Consortium via PRIDE – a publicly available repository for MS-based proteomics data. Mass spectrometric data generated from an entire monastic library and archive is of immense value to integrate with multiple case studies aiming at understanding parchment production and use in medieval Europe. Paper linked with data: Ruffini-Ronzani, N., Nieus, J.F., Soncin, S., Hickinbotham, S., Dieu, M., Bouhy, J., Charles, C., Ruzzier, C., Falmagne, T., Hermand, X., Collins, M.J. and Deparis, O 2021. A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium. 'Royal Society Open Science, 8'(6), p.210210.

Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.

The present work deals with the reconstruction of the flood wave that hit Mandra town (Athens, Greece) on 15 November 2017, using the framework of forensic hydrology. The flash flood event was caused by a huge storm event with a high level of spatial and temporal variability, which was part of the Medicane Numa-Zenon. The reconstruction included: (a) the post-event collection of 44 maximum water depth traces in the town; and (b) the hydrodynamic simulation employing the HEC-RAS and MIKE FLOOD software. The derived open dataset (which also includes additional data required for hydrodynamic modeling) is shared with the community for possible use as a benchmark case for flood model developers. With regards to the modeling issues, we investigate the calibration strategies in computationally demanding cases, and test whether the calibrated parameters can be blindly transferred to another simulator (informed modeling). Regarding the calibration, it seems that the coupling of an initial screening phase with a simple grid-search algorithm is efficient. On the other hand, the informed modeling concept does not work for our study area: every numerical model has its own dynamics while the parameters are of grey-box nature. As a result, the modeler should always be skeptical about their global use.

This commentary explores features of the "Saraga" article and open dataset, discussing some of the issues arising. I argue that the CompMusic project and this resulting dataset are impressive for their sensitivity to cultural specificities of the Hindustani and Carnatic musical styles; for example, the dataset includes manual annotations based on music theoretical concepts from within the styles, rather than imposing conceptual categories from outside. However, I propose there are aspects of the dataset's manual annotations that require clarification in order for them to be used as ground truths by other researchers. In addition, I raise questions regarding the representativeness of the dataset - an issue that has ethical implications. [ABSTRACT FROM AUTHOR]

The dataset is a systematic inventory of chamber tombs in Syro-Palestine from the first millennium BCE and CE. It contains information on the form, equipment and location of collective burial sites discovered in Israel, Jordan, Lebanon, Syria and parts of Turkey. This data was collected from published excavation reports and monographs on over 650 sites. This dataset can serve as a starting point for studying the regional diversity of material culture and burial practices and their changes over the centuries. Funding statement: This work was supported by the Polish National Science Centre under Grant 2016/21/N/HS3/00040 entitled “End of Tradition of Chamber Tombs as an Indication of Cultural Change Among the Inhabitants of Syro-Palestine in the Roman Period.”

Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7.

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Recently, unmanned aerial vehicles (UAVs) have been broadly applied to the remote sensing field. For a great number of UAV images, deep learning has been reinvigorated and performed many results in agricultural applications. The popular image datasets for deep learning model training are generated for general purpose use, in which the objects, views, and applications are for ordinary scenarios. However, UAV images possess different patterns of images mostly from a look-down perspective. This paper provides a verified annotated dataset of UAV images that are described in data acquisition, data preprocessing, and a showcase of a CNN classification. The dataset collection consists of one multi-rotor UAV platform by flying a planned scouting routine over rice paddies. This paper introduces a semi-auto annotation method with an ExGR index to generate the training data of rice seedlings. For demonstration, this study modified a classical CNN architecture, VGG-16, to run a patch-based rice seedling detection. The k-fold cross-validation was employed to obtain an 80/20 dividing ratio of training/test data. The accuracy of the network increases with the increase of epoch, and all the divisions of the cross-validation dataset achieve a 0.99 accuracy. The rice seedling dataset provides the training-validation dataset, patch-based detection samples, and the ortho-mosaic image of the field.

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

The accuracy of biometric recognition in unconstrained scenarios has been a major concern for a large number of researchers. Despite such efforts, no system can recognise in a fully automated manner human beings in totally wild conditions such as in surveillance environments. Several sets of degraded data have been made available to the research community, where the reported performance by state‐of‐the‐art algorithms is already saturated, suggesting that these sets do not reflect faithfully the conditions in such hard settings. To this end, the authors introduce the QUIS‐CAMPI data feed, comprising samples automatically acquired by an outdoor visual surveillance system, with subjects on‐the‐move and at‐a‐distance (up to 50 m). Also, they supply a high‐quality set of enrolment data. When compared to similar data sources, the major novelties of QUIS‐CAMPI are: (i) biometric samples are acquired in a fully automatic way; (ii) it is an open dataset, i.e. the number of probe images and enroled subjects grow on a daily basis; and (iii) it contains multi‐biometric traits. The ensemble properties of QUIS‐CAMPI ensure that the data span a representative set of covariate factors of real‐world scenarios, making it a valuable tool for developing and benchmarking biometric recognition algorithms capable of working in unconstrained scenarios. [ABSTRACT FROM AUTHOR]

Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). Acquisition and validation methods: T1‐ and T2‐weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2‐weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open‐source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. Data format and usage notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. Potential applications: The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT‐like data (sCT). To give some examples: The T2‐weighted MR images with their consensus delineations can directly be used as a template in an existing atlas‐based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas‐based sCT algorithm or for validation of sCT algorithm. [ABSTRACT FROM AUTHOR]