731,195 research outputs found
Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
We address the problem of scene classification from optical remote sensing
(RS) images based on the paradigm of hierarchical metric learning. Ideally,
supervised metric learning strategies learn a projection from a set of training
data points so as to minimize intra-class variance while maximizing inter-class
separability to the class label space. However, standard metric learning
techniques do not incorporate the class interaction information in learning the
transformation matrix, which is often considered to be a bottleneck while
dealing with fine-grained visual categories. As a remedy, we propose to
organize the classes in a hierarchical fashion by exploring their visual
similarities and subsequently learn separate distance metric transformations
for the classes present at the non-leaf nodes of the tree. We employ an
iterative max-margin clustering strategy to obtain the hierarchical
organization of the classes. Experiment results obtained on the large-scale
NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of
the proposed hierarchical metric learning based RS scene recognition strategy
in comparison to the standard approaches.Comment: Undergoing revision in GRS
PromotionLens: Inspecting Promotion Strategies of Online E-commerce via Visual Analytics
Promotions are commonly used by e-commerce merchants to boost sales. The
efficacy of different promotion strategies can help sellers adapt their
offering to customer demand in order to survive and thrive. Current approaches
to designing promotion strategies are either based on econometrics, which may
not scale to large amounts of sales data, or are spontaneous and provide little
explanation of sales volume. Moreover, accurately measuring the effects of
promotion designs and making bootstrappable adjustments accordingly remains a
challenge due to the incompleteness and complexity of the information
describing promotion strategies and their market environments. We present
PromotionLens, a visual analytics system for exploring, comparing, and modeling
the impact of various promotion strategies. Our approach combines
representative multivariant time-series forecasting models and well-designed
visualizations to demonstrate and explain the impact of sales and promotional
factors, and to support "what-if" analysis of promotions. Two case studies,
expert feedback, and a qualitative user study demonstrate the efficacy of
PromotionLens.Comment: IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE
VIS 2022
Doctor of Philosophy
dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets
Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation
Current state-of-the-art point cloud-based perception methods usually rely on
large-scale labeled data, which requires expensive manual annotations. A
natural option is to explore the unsupervised methodology for 3D perception
tasks. However, such methods often face substantial performance-drop
difficulties. Fortunately, we found that there exist amounts of image-based
datasets and an alternative can be proposed, i.e., transferring the knowledge
in the 2D images to 3D point clouds. Specifically, we propose a novel approach
for the challenging cross-modal and cross-domain adaptation task by fully
exploring the relationship between images and point clouds and designing
effective feature alignment strategies. Without any 3D labels, our method
achieves state-of-the-art performance for 3D point cloud semantic segmentation
on SemanticKITTI by using the knowledge of KITTI360 and GTA5, compared to
existing unsupervised and weakly-supervised baselines.Comment: 12 pages,4 figures,accepte
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"Call a Teenager⊠That's What I Do!" - Grandchildren Help Older Adults Use New Technologies: Qualitative Study.
BackgroundAlthough family technical support seems intuitive, there is very little research exploring this topic.ObjectiveThe objective of this study was to conduct a subanalysis of data collected from a large-scale qualitative project regarding older adults' experiences in using health information technology. Specifically, the subanalysis explored older adults' experiences with technology support from family members to inform strategies for promoting older adults' engagement with new health technologies. Although the primary analysis of the original study was theoretically driven, this paper reports results from an inductive, open-coding analysis.MethodsThis is a subanalysis of a major code identified unexpectedly from a qualitative study investigating older adults' use experience of a widespread health technology, the patient portal. A total of 24 older patients (â„65 years) with multiple chronic conditions (Charlson Comorbidity Index >2) participated in focus groups conducted at the patients' primary clinic. While conducting the primary theoretically driven analysis, coders utilized an open-coding approach to ensure important ideas not reflected in the theoretical code book were captured. Open coding resulted in 1 code: family support. This subanalysis further categorized family support by who provided tech support, how tech support was offered, and the opinions of older participants about receiving family tech support.ResultsThe participants were not specifically asked about family support, yet themes around family assistance and encouragement for technology emerged from every focus group. Participants repeatedly mentioned that they called their grandchildren and adult children if they needed help with technology. Participants also reported that family members experienced difficulty when teaching technology use. Family members struggled to explain simple technology tasks and were frustrated by the slow teaching process.ConclusionsThe results suggest that older adults ask their family members, particularly grandchildren, to support them in the use of new technologies. However, family may experience difficulties in providing this support. Older adults will be increasingly expected to use health technologies, and family members may help with tech support. Providers and health systems should consider potential family support and engagement strategies to foster adoption and use among older patients
Improving Multimodal Datasets with Image Captioning
Massive web datasets play a key role in the success of large vision-language
models like CLIP and Flamingo. However, the raw web data is noisy, and existing
filtering methods to reduce noise often come at the expense of data diversity.
Our work focuses on caption quality as one major source of noise, and studies
how generated captions can increase the utility of web-scraped datapoints with
nondescript text. Through exploring different mixing strategies for raw and
generated captions, we outperform the best filtering method proposed by the
DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a
candidate pool of 128M image-text pairs. Our best approach is also 2x better at
Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an
effective source of text supervision. In experimenting with different image
captioning models, we also demonstrate that the performance of a model on
standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable
indicator of the utility of the captions it generates for multimodal training.
Finally, our experiments with using generated captions at DataComp's large
scale (1.28B image-text pairs) offer insights into the limitations of synthetic
text, as well as the importance of image curation with increasing training data
quantity
ANALISIS ADOPSI E-COMMERCE TERHADAP KINERJA UMKM DESA PLERET
The spread of Covid-19 has had a negative impact on the business world, both small, medium and large scale. One of the strategies to deal with the impact of Covid-19 for SMEs is by adapting to technological advances 4.0 in order to keep up with the times.The main purpose of this study is to find out how the adoption of e-commerce in MSMEs in Pleret Village. In addition, to analyze the impact of adoption on the performance of SMEs. The approach used in this research is descriptive exploratory with the aim of exploring the field of study. The data analysis technique used was thematic analysis (Data Collection, Data Reduction, Data Presentation, Conclusions).The results of this study indicate that technology, organizational and environmental are factors that influence the adoption of e-commerce. However, the most dominant factor is the technology factor. As for the factors that affect the performance of MSMEs are increased sales, increased capital, increased labor, and increased profits, as well as an increase in the market. However, the most dominant factor of the impact of e-commerce adoption is sales growth
Multilevel latent class analysis for large-scale educational assessment data: Exploring the relation between the curriculum and students' mathematical strategies
A ïŹrst application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint inïŹuence of teacher and student variables. Using data from the 2011 assessment of Dutch primary schoolsâ mathematics, this study explores the relation between the curriculum as reported by 107 teachers and the strategy choices of their 1,619 students, while controlling for student characteristics. Considerable teacher effects are demonstrated, as well as signiïŹcant relations between the intended as well as enacted curriculum and studentsâ strategy use. Implications of these results for both more theoretical and practical educational research are discussed, as are several issues in applying MLCA and possibilities for applying MLCA to different types of educational data.Development Psychopathology in context: schoo
Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review
The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of SĂŁo Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotypeâenvironment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions
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