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Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations. 基于卫星多频微波亮度温度观测的北半球土壤冻融动态深度学习估计。
IF 3.1 Q2 Computer Science Pub Date : 2023-11-17 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1243559
Kellen Donahue, John S Kimball, Jinyang Du, Fredrick Bunt, Andreas Colliander, Mahta Moghaddam, Jesse Johnson, Youngwook Kim, Michael A Rawlins

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0-5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016-2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.

卫星微波传感器非常适合监测景观冻融(FT)转变,因为它对主要冻结和解冻条件之间液态水丰度的变化具有强烈的亮度、温度(TB)或反向散射响应。FT检索也是一个敏感的气候指标,具有很强的生物物理重要性。然而,检索算法很难将土壤的FT状态与积雪和植被等上覆特征区分开来,而多变的土地条件也会降低性能。在此,我们采用AMSR2和SMAP TB记录驱动的多层卷积神经网络深度学习模型,并对地表(~0-5 cm)土壤温度FT观测数据进行训练。土壤FT状态被分类为当地早上(上午6点)和晚上(下午6点)的条件,对应于SMAP下降和上升的轨道立交桥,映射到跨越五年(2016-2020)记录和北半球域的9公里极地网格。使用针对FT观测训练数据优化的模型成本函数,推导出冻结或解冻条件概率的连续变量估计。使用组合多频(1.4、18.7、36.5 GHz) TB获得的模型结果比仅使用单一传感器或单频TB输入的其他模型获得的土壤FT精度最高。此外,与仅使用AMSR2 TB输入的模型结果相比,SMAP l波段(1.4 GHz) TB提供了更好的土壤FT信息和性能增益。所得土壤FT分类结果与ERA5再分析结果(平均准确率,MPA: 92.7%)和现场气象站(MPA: 91.0%)的土壤FT分类结果一致。土壤FT的准确性在上午和下午的预测之间以及不同的土地覆盖和季节之间总体上是一致的。该模型对区域气象站测量的FT精度也优于ERA5(91.0%比86.1% MPA)。然而,在复杂地形中,模型置信度较低,在这种地形中,FT的空间异质性可能低于有效模型粒度。我们的研究结果为绘制土壤FT动态提供了高水平的精度,以提高对复杂季节转变及其对生态过程和气候反馈的影响的理解,并有可能为地球系统模型预测提供信息。
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引用次数: 0
Integrating geometries of ReLU feedforward neural networks ReLU前馈神经网络的几何积分
Q2 Computer Science Pub Date : 2023-11-14 DOI: 10.3389/fdata.2023.1274831
Yajing Liu, Turgay Caglar, Christopher Peterson, Michael Kirby
This paper investigates the integration of multiple geometries present within a ReLU-based neural network. A ReLU neural network determines a piecewise affine linear continuous map, M , from an input space ℝ m to an output space ℝ n . The piecewise behavior corresponds to a polyhedral decomposition of ℝ m . Each polyhedron in the decomposition can be labeled with a binary vector (whose length equals the number of ReLU nodes in the network) and with an affine linear function (which agrees with M when restricted to points in the polyhedron). We develop a toolbox that calculates the binary vector for a polyhedra containing a given data point with respect to a given ReLU FFNN. We utilize this binary vector to derive bounding facets for the corresponding polyhedron, extraction of “active” bits within the binary vector, enumeration of neighboring binary vectors, and visualization of the polyhedral decomposition (Python code is available at https://github.com/cglrtrgy/GoL_Toolbox ). Polyhedra in the polyhedral decomposition of ℝ m are neighbors if they share a facet. Binary vectors for neighboring polyhedra differ in exactly 1 bit. Using the toolbox, we analyze the Hamming distance between the binary vectors for polyhedra containing points from adversarial/nonadversarial datasets revealing distinct geometric properties. A bisection method is employed to identify sample points with a Hamming distance of 1 along the shortest Euclidean distance path, facilitating the analysis of local geometric interplay between Euclidean geometry and the polyhedral decomposition along the path. Additionally, we study the distribution of Chebyshev centers and related radii across different polyhedra, shedding light on the polyhedral shape, size, clustering, and aiding in the understanding of decision boundaries.
本文研究了基于relu的神经网络中存在的多种几何图形的集成。一个ReLU神经网络确定一个分段仿射线性连续映射M,从一个输入空间M到一个输出空间。这种分段行为对应于一个多面体的分解。分解中的每个多面体都可以用一个二进制向量(其长度等于网络中ReLU节点的数量)和一个仿射线性函数(当限制为多面体中的点时,它与M一致)来标记。我们开发了一个工具箱,用于计算包含给定数据点的多面体相对于给定ReLU FFNN的二进制向量。我们利用这个二进制向量来推导相应多面体的边界切面,提取二进制向量中的“活动”位,枚举相邻的二进制向量,以及多面体分解的可视化(Python代码可在https://github.com/cglrtrgy/GoL_Toolbox获得)。在多面体分解中,如果多面体共用一个面,则多面体是相邻体。相邻多面体的二进制向量相差1位。使用工具箱,我们分析了包含来自对抗性/非对抗性数据集的点的多面体的二进制向量之间的汉明距离,揭示了不同的几何特性。采用对分法沿最短欧氏距离路径识别汉明距离为1的样本点,便于分析欧氏几何与路径多面体分解之间的局部几何相互作用。此外,我们研究了切比雪夫中心和相关半径在不同多面体上的分布,揭示了多面体的形状、大小、聚类,并有助于理解决策边界。
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引用次数: 0
Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse 迈向理解全球脑数据治理:支撑全球脑数据治理话语的伦理立场
Q2 Computer Science Pub Date : 2023-11-09 DOI: 10.3389/fdata.2023.1240660
Damian Eke, Paschal Ochang, Bernd Carsten Stahl
Introduction The study of the brain continues to generate substantial volumes of data, commonly referred to as “big brain data,” which serves various purposes such as the treatment of brain-related diseases, the development of neurotechnological devices, and the training of algorithms. This big brain data, generated in different jurisdictions, is subject to distinct ethical and legal principles, giving rise to various ethical and legal concerns during collaborative efforts. Understanding these ethical and legal principles and concerns is crucial, as it catalyzes the development of a global governance framework, currently lacking in this field. While prior research has advocated for a contextual examination of brain data governance, such studies have been limited. Additionally, numerous challenges, issues, and concerns surround the development of a contextually informed brain data governance framework. Therefore, this study aims to bridge these gaps by exploring the ethical foundations that underlie contextual stakeholder discussions on brain data governance. Method In this study we conducted a secondary analysis of interviews with 21 neuroscientists drafted from the International Brain Initiative (IBI), LATBrain Initiative and the Society of Neuroscientists of Africa (SONA) who are involved in various brain projects globally and employing ethical theories. Ethical theories provide the philosophical frameworks and principles that inform the development and implementation of data governance policies and practices. Results The results of the study revealed various contextual ethical positions that underscore the ethical perspectives of neuroscientists engaged in brain data research globally. Discussion This research highlights the multitude of challenges and deliberations inherent in the pursuit of a globally informed framework for governing brain data. Furthermore, it sheds light on several critical considerations that require thorough examination in advancing global brain data governance.
对大脑的研究不断产生大量的数据,通常被称为“大大脑数据”,这些数据服务于各种目的,如治疗大脑相关疾病、开发神经技术设备和训练算法。这些在不同司法管辖区产生的大脑大数据受到不同的伦理和法律原则的约束,在合作过程中产生了各种各样的伦理和法律问题。理解这些道德和法律原则和关切是至关重要的,因为它促进了全球治理框架的发展,目前在这一领域缺乏。虽然先前的研究主张对大脑数据治理进行背景检查,但此类研究是有限的。此外,围绕上下文信息大脑数据治理框架的发展,还有许多挑战、问题和关注。因此,本研究旨在通过探索背景利益相关者关于大脑数据治理讨论的伦理基础来弥合这些差距。在本研究中,我们对21位来自国际脑倡议(IBI)、拉丁脑倡议(LATBrain Initiative)和非洲神经科学家协会(SONA)的神经科学家的访谈进行了二次分析,他们参与了全球各种脑项目,并采用了伦理理论。伦理理论为数据治理政策和实践的发展和实施提供了哲学框架和原则。研究结果揭示了各种背景伦理立场,强调了全球从事脑数据研究的神经科学家的伦理观点。这项研究强调了在追求一个全球知情的大脑数据管理框架时所固有的众多挑战和审议。此外,它还阐明了在推进全球大脑数据治理时需要彻底检查的几个关键考虑因素。
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引用次数: 0
A community focused approach toward making healthy and affordable daily diet recommendations 以社区为中心的方法,提供健康和负担得起的日常饮食建议
Q2 Computer Science Pub Date : 2023-11-06 DOI: 10.3389/fdata.2023.1086212
Joe Germino, Annalisa Szymanski, Ronald Metoyer, Nitesh V. Chawla
Introduction Maintaining an affordable and nutritious diet can be challenging, especially for those living under the conditions of poverty. To fulfill a healthy diet, consumers must make difficult decisions within a complicated food landscape. Decisions must factor information on health and budget constraints, the food supply and pricing options at local grocery stores, and nutrition and portion guidelines provided by government services. Information to support food choice decisions is often inconsistent and challenging to find, making it difficult for consumers to make informed, optimal decisions. This is especially true for low-income and Supplemental Nutrition Assistance Program (SNAP) households which have additional time and cost constraints that impact their food purchases and ultimately leave them more susceptible to malnutrition and obesity. The goal of this paper is to demonstrate how the integration of data from local grocery stores and federal government databases can be used to assist specific communities in meeting their unique health and budget challenges. Methods We discuss many of the challenges of integrating multiple data sources, such as inconsistent data availability and misleading nutrition labels. We conduct a case study using linear programming to identify a healthy meal plan that stays within a limited SNAP budget and also adheres to the Dietary Guidelines for Americans. Finally, we explore the main drivers of cost of local food products with emphasis on the nutrients determined by the USDA as areas of focus: added sugars, saturated fat, and sodium. Results and discussion Our case study results suggest that such an optimization model can be used to facilitate food purchasing decisions within a given community. By focusing on the community level, our results will inform future work navigating the complex networks of food information to build global recommendation systems.
维持负担得起的营养饮食可能具有挑战性,特别是对那些生活在贫困条件下的人来说。为了实现健康饮食,消费者必须在复杂的食品环境中做出艰难的决定。决策必须考虑健康和预算限制、当地杂货店的食品供应和价格选择以及政府服务部门提供的营养和份量指南等方面的信息。支持食品选择决策的信息往往不一致,很难找到,这使得消费者难以做出明智的最佳决定。对于低收入家庭和参加补充营养援助计划(SNAP)的家庭来说尤其如此,这些家庭有额外的时间和成本限制,影响了他们的食品购买,最终使他们更容易营养不良和肥胖。本文的目的是演示如何将来自地方杂货店和联邦政府数据库的数据整合起来,以帮助特定社区应对其独特的健康和预算挑战。我们讨论了整合多个数据源的许多挑战,如数据可用性不一致和误导营养标签。我们进行了一个案例研究,使用线性规划来确定一个健康的膳食计划,保持在有限的SNAP预算内,并遵守美国人的膳食指南。最后,我们探讨了当地食品成本的主要驱动因素,重点关注美国农业部确定的营养成分:添加糖、饱和脂肪和钠。结果和讨论我们的案例研究结果表明,这种优化模型可以用于促进特定社区内的食品购买决策。通过关注社区层面,我们的结果将为未来导航复杂的食品信息网络以构建全球推荐系统的工作提供信息。
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引用次数: 0
impresso Text Reuse at Scale. An interface for the exploration of text reuse data in semantically enriched historical newspapers 大规模的文本重用。在语义丰富的历史报纸中探索文本重用数据的接口
Q2 Computer Science Pub Date : 2023-11-03 DOI: 10.3389/fdata.2023.1249469
Marten Düring, Matteo Romanello, Maud Ehrmann, Kaspar Beelen, Daniele Guido, Brecht Deseure, Estelle Bunout, Jana Keck, Petros Apostolopoulos
Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often supported by interactive visualizations which highlight relations and differences between text segments. In this paper, we build on earlier work in this domain. We present impresso Text Reuse at Scale, the to our knowledge first interface which integrates text reuse data with other forms of semantic enrichment to enable a versatile and scalable exploration of intertextual relations in historical newspaper corpora. The Text Reuse at Scale interface was developed as part of the impresso project and combines powerful search and filter operations with close and distant reading perspectives. We integrate text reuse data with enrichments derived from topic modeling, named entity recognition and classification, language and document type detection as well as a rich set of newspaper metadata. We report on historical research objectives and common user tasks for the analysis of historical text reuse data and present the prototype interface together with the results of a user evaluation.
文本重用揭示了大型语料库中文本的有意义的重复。人文学者使用文本再利用来研究,例如,有影响力的文本的后接受或揭示历史媒体不断发展的出版实践。这种研究经常得到交互式可视化的支持,它突出了文本段之间的关系和差异。在本文中,我们以该领域的早期工作为基础。我们提出了impresso大规模文本重用,这是我们的知识第一接口,它将文本重用数据与其他形式的语义丰富集成在一起,从而能够对历史报纸语料库中的互文关系进行通用和可扩展的探索。大规模文本重用界面是作为impresso项目的一部分开发的,它结合了强大的搜索和过滤操作以及近距离和远距离阅读视角。我们将文本重用数据与来自主题建模、命名实体识别和分类、语言和文档类型检测以及一组丰富的报纸元数据的丰富内容集成在一起。我们报告了历史研究目标和常见的用户任务,用于分析历史文本重用数据,并提供了原型界面以及用户评估结果。
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引用次数: 0
Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks 利用唇黏膜图像转移学习卷积神经网络进行无创贫血检测
Q2 Computer Science Pub Date : 2023-11-03 DOI: 10.3389/fdata.2023.1291329
Mohammed Mansour, Turker Berk Donmez, Mustafa Kutlu, Shekhar Mahmud
Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.
贫血被定义为健康人红细胞数量或血红蛋白浓度低于正常水平。尽管目前还没有可量化的测量方法,但皮肤苍白程度的增加可能因肤色而异。皮肤的苍白在角质层较薄的地方最为明显,如口腔、嘴唇或结膜的内部。这项工作的重点是贫血相关的苍白及其与血细胞计数值和人工智能的关系。在这项研究中,使用迁移学习和卷积神经网络(CNN)实现了一种深度学习方法,其中对VGG16、Xception、MobileNet和ResNet50架构进行了预训练,以使用唇粘膜图像预测贫血。共有138名志愿者(100名女性和38名男性)参与了开发数据集的工作,该数据集包含两个图像类别:健康和贫血。首先对仅可见嘴巴区域的单帧图像进行处理,进行数据论证,然后应用CNN模型对数据集嘴唇图像进行分类。采用统计指标来区分模型在准确性、精度、Recal和F1评分方面的表现。在使用的CNN算法中,发现Xception对唇形图像的分类准确率为99.28%,提供了最好的结果。其他CNN架构对于MobileNet的准确率为96.38%,对于ResNet %的准确率为95.65%,对于VGG16的准确率为92.39%。我们的研究结果表明,可以使用深度学习方法从单个嘴唇图像中诊断贫血。该数据集将在未来得到增强,以允许实时分类。
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引用次数: 0
No longer hype, not yet mainstream? Recalibrating city digital twins' expectations and reality: a case study perspective 不再炒作,还不是主流?重新校准城市数字孪生的期望和现实:一个案例研究的视角
Q2 Computer Science Pub Date : 2023-11-02 DOI: 10.3389/fdata.2023.1236397
Stefano Calzati
While the concept of digital twin has already consolidated in industry, its spinoff in the urban environment—in the form of a City Digital Twin (CDT)—is more recent. A CDT is a dynamic digital model of the physical city whereby the physical and the digital are integrated in both directions, thus mutually affecting each other in real time. Replicating the path of smart cities, literature remarks that agendas and discourses around CDTs remain (1) tech-centered, that is, focused on overcoming technical limitations and lacking a proper sociotechnical contextualization of digital twin technologies; (2) practice-first, entailing hands-on applications without a long-term strategic governance for the management of these same technologies. Building on that, the goal of this article is to move beyond high-level conceptualizations of CDT to (a) get a cognizant understanding of what a CDT can do, how, and for whom; (b) map the current state of development and implementation of CDTs in Europe. This will be done by looking at three case studies—Dublin, Helsinki, and Rotterdam—often considered as successful examples of CDTs in Europe. Through exiting literature and official documents, as well as by relying on primary interviews with tech experts and local officials, the article explores the maturity of these CDTs, along the Gartner's hype-mainstream curve of technological innovations. Findings show that, while all three municipalities have long-term plans to deliver an integrated, cyber-physical real-time modeling of the city, currently their CDTs are still at an early stage of development. The focus remains on technical barriers—e.g., integration of different data sources—overlooking the societal dimension, such as the systematic involvement of citizens. As for the governance, all cases embrace a multistakeholder approach; yet CDTs are still not used for policymaking and it remains to see how the power across stakeholders will be distributed in terms of access to, control of, and decisions about CDTs.
虽然数字孪生的概念已经在工业中得到了巩固,但它在城市环境中的衍生——城市数字孪生(CDT)的形式——是最近才出现的。CDT是物理城市的动态数字模型,物理和数字在两个方向上融合,从而实时相互影响。复制智慧城市的路径,文献评论围绕cdt的议程和话语仍然(1)以技术为中心,即专注于克服技术限制,缺乏适当的数字孪生技术的社会技术背景;(2)实践优先,需要实际操作的应用程序,而没有长期的战略治理来管理这些相同的技术。在此基础上,本文的目标是超越CDT的高级概念化,以(a)对CDT可以做什么、如何做以及为谁做有一个认识上的理解;(b)绘制欧洲发展和执行清洁发展技术的现状图。这将通过考察都柏林、赫尔辛基和鹿特丹这三个通常被认为是欧洲cdt成功范例的案例来完成。通过现有文献和官方文件,以及依靠对技术专家和地方官员的初步采访,本文沿着Gartner的技术创新炒作-主流曲线探索了这些cdt的成熟度。调查结果显示,虽然这三个城市都有提供城市综合网络物理实时建模的长期计划,但目前它们的cdt仍处于早期发展阶段。重点仍然是技术障碍,例如。不同数据源的整合——忽略了社会维度,例如公民的系统参与。至于治理,所有案例都采用多利益相关者方法;然而,cdt仍未被用于政策制定,在cdt的获取、控制和决策方面,利益相关者之间的权力如何分配仍有待观察。
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引用次数: 0
A fast parallelized DBSCAN algorithm based on OpenMp for detection of criminals on streaming services 一种基于OpenMp的快速并行DBSCAN算法用于流媒体服务中的犯罪分子检测
Q2 Computer Science Pub Date : 2023-10-31 DOI: 10.3389/fdata.2023.1292923
Lesia Mochurad, Andrii Sydor, Oleh Ratinskiy
Introduction Streaming services are highly popular today. Millions of people watch live streams or videos and listen to music. Methods One of the most popular streaming platforms is Twitch, and data from this type of service can be a good example for applying the parallel DBSCAN algorithm proposed in this paper. Unlike the classical approach to neighbor search, the proposed one avoids redundancy, i.e., the repetition of the same calculations. At the same time, this algorithm is based on the classical DBSCAN method with a full search for all neighbors, parallelization by subtasks, and OpenMP parallel computing technology. Results In this work, without reducing the accuracy, we managed to speed up the solution based on the DBSCAN algorithm when analyzing medium-sized data. As a result, the acceleration rate tends to the number of cores of a multicore computer system and the efficiency to one. Discussion Before conducting numerical experiments, theoretical estimates of speed-up and efficiency were obtained, and they aligned with the results obtained, confirming their validity. The quality of the performed clustering was verified using the silhouette value. All experiments were conducted using different percentages of medium-sized datasets. The prospects of applying the proposed algorithm can be obtained in various fields such as advertising, marketing, cybersecurity, and sociology. It is worth mentioning that datasets of this kind are often used for detecting fraud on the Internet, making an algorithm capable of considering all neighbors a useful tool for such research.
流媒体服务在今天非常受欢迎。数百万人观看直播或视频,听音乐。方法Twitch是最流行的流媒体平台之一,该服务的数据可以作为应用本文提出的并行DBSCAN算法的一个很好的例子。与传统的邻居搜索方法不同,该方法避免了冗余,即重复相同的计算。同时,该算法基于经典的DBSCAN方法,充分搜索所有邻居,采用子任务并行化和OpenMP并行计算技术。结果在不降低准确率的情况下,在分析中等规模数据时,我们成功地提高了基于DBSCAN算法的求解速度。因此,加速速率趋向于多核计算机系统的核数,效率趋向于1。在进行数值实验之前,得到了加速和效率的理论估计,并与所得结果相吻合,证实了其有效性。使用轮廓值验证所执行聚类的质量。所有实验都使用不同百分比的中型数据集进行。该算法在广告、市场营销、网络安全、社会学等领域具有广泛的应用前景。值得一提的是,这类数据集经常被用来检测互联网上的欺诈行为,这使得能够考虑所有邻居的算法成为此类研究的有用工具。
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引用次数: 0
An overview of video recommender systems: state-of-the-art and research issues. 视频推荐系统综述:最新技术和研究问题。
IF 3.1 Q2 Computer Science Pub Date : 2023-10-30 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1281614
Sebastian Lubos, Alexander Felfernig, Markus Tautschnig

Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.

视频平台已经成为各种应用中不可或缺的组成部分,服务于娱乐、电子学习、企业培训、在线文档和新闻提供等各种目的。随着视频内容的数量和复杂性不断增长,个性化访问功能的需求成为确保高效内容消费的必然要求。为了满足这一需求,推荐系统已经成为提供个性化视频访问的有用工具。通过利用过去用户特定的视频消费数据和类似用户的偏好,这些系统在推荐与个人用户高度相关的视频方面表现出色。本文全面概述了视频推荐系统(VRS)的现状,探讨了所使用的算法、它们的应用和相关方面。除了对现有方法的深入分析之外,本综述还解决了该领域内尚未解决的研究挑战。这些未开发的领域为进步和创新提供了令人兴奋的机会,旨在提高个性化视频推荐的准确性和有效性。总的来说,本文为视频领域的研究人员、从业者和利益相关者提供了宝贵的资源。它提供了对前沿算法、成功应用和值得进一步探索的领域的见解,以推进视频推荐领域。
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引用次数: 0
Recommender systems for sustainability: overview and research issues. 可持续发展的推荐系统:概述和研究问题。
IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2023-10-30 eCollection Date: 2023-01-01 DOI: 10.3389/fdata.2023.1284511
Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran, Seda Polat-Erdeniz, Sebastian Lubos, Merfat El Mansi, Damian Garber, Viet-Man Le

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.

可持续发展目标(sdg)被认为是一项普遍的行动呼吁,其总体目标是保护地球,消除贫困,确保所有人的和平与繁荣。为了实现这些目标,不同的人工智能技术发挥了重要作用。具体来说,推荐系统可以为组织和个人提供支持,以实现定义的目标。推荐系统集成了人工智能技术,如机器学习、可解释的人工智能(XAI)、基于案例的推理和约束解决,以便从潜在的大量选项中找到并解释与用户相关的替代方案。在本文中,我们总结了应用推荐系统来支持实现可持续发展目标的最新进展。在此背景下,我们讨论了未来研究的开放性问题。
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Frontiers in Big Data
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