Personalized Food Image Classification: Benchmark Datasets and New Baseline

Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural networks to train on generic food image datasets that do not reflect the dynamism of real-life food consumption patterns, in which food images appear sequentially over time, reflecting the progression of what an individual consumes. Personalized food classification aims to address this problem by training a deep neural network using food images that reflect the consumption pattern of each individual. However, this problem is under-explored and there is a lack of benchmark datasets with individualized food consumption patterns due to the difficulty in data collection. In this work, we first introduce two benchmark personalized datasets including the Food101-Personal, which is created based on surveys of daily dietary patterns from participants in the real world, and the VFNPersonal, which is developed based on a dietary study. In addition, we propose a new framework for personalized food image classification by leveraging self-supervised learning and temporal image feature information. Our method is evaluated on both benchmark datasets and shows improved performance compared to existing works. The dataset has been made available at: https://skynet.ecn.purdue.edu/~pan161/dataset_personal.htmlComment: Accepted by IEEE Asilomar conference (2023

Muti-Stage Hierarchical Food Classification

Food image classification serves as a fundamental and critical step in image-based dietary assessment, facilitating nutrient intake analysis from captured food images. However, existing works in food classification predominantly focuses on predicting 'food types', which do not contain direct nutritional composition information. This limitation arises from the inherent discrepancies in nutrition databases, which are tasked with associating each 'food item' with its respective information. Therefore, in this work we aim to classify food items to align with nutrition database. To this end, we first introduce VFN-nutrient dataset by annotating each food image in VFN with a food item that includes nutritional composition information. Such annotation of food items, being more discriminative than food types, creates a hierarchical structure within the dataset. However, since the food item annotations are solely based on nutritional composition information, they do not always show visual relations with each other, which poses significant challenges when applying deep learning-based techniques for classification. To address this issue, we then propose a multi-stage hierarchical framework for food item classification by iteratively clustering and merging food items during the training process, which allows the deep model to extract image features that are discriminative across labels. Our method is evaluated on VFN-nutrient dataset and achieve promising results compared with existing work in terms of both food type and food item classification.Comment: accepted for ACM MM 2023 Madim

中国東部における地表温度と寒冷前線構造に関する都市の熱的・力学的効果

Tohoku University山崎剛課

Recognizing Dew as an Indicator and an Improver of Near-Surface Air Quality

The relationship between dew and airborne particles is important in urban ecosystems, but the capability of dew to remove airborne particles remains unclear. During 2015 in Changchun, China, 74 dew and particle samples were collected simultaneously to investigate their chemical characteristics under normal, fog, and haze conditions. Analyses included measuring total dissolved solids, total suspended particulates, PM2.5 and PM10 concentrations, major cations (NH4+, Na+, K+, Ca2+, and Mg2+), major anions (F−, Cl−, SO42-, and NO3-), and organic and elemental carbon. Results showed that air quality deteriorated during haze but improved in fog. The particle size distributions, major cations, and carbonaceous species documented in the dew and airborne particles demonstrated consistent synchronous patterns with values descending in the order haze > normal > fog conditions. We found that dew is a good indicator of near-surface air quality. Specifically, its water-soluble ions and carbonaceous species could be used to distinguish emission sources and to identify the presence of secondary organic carbon. Dew is more effective at removing airborne particles in normal weather than in haze or fog conditions; PM2.5 removal rates were 21.5%, 15.2%, and 13.7% on normal, foggy, and hazy days, respectively. Dew condensation processes reduce concentrations of gaseous and particulate pollutants in the near-surface layer

Understanding Public Online Donations on Social Media during the Pandemic: A Social Presence Theory Perspective

The COVID-19 pandemic has had a huge impact on the global economy and health care, but online donations from the public on social media have increased significantly. However, the role of social presence in motivating people to donate online during the pandemic has been largely unexplored. This study examines the relationship between social presence on social media and online donation behavior during the pandemic using social presence theory. We explore the interplay between social presence, perceived threat, social properties of social media, and donation intentions. The results showed that social presence based on social media, perception of others and social interaction significantly affected social media online donation participation, and the perceived threat of COVID-19 significantly moderated online donation participation. Our research contributes to the understanding of online donation behavior during a pandemic crisis and provides insights into how social media can be leveraged for effective donation campaigns

An Explorative Study on Document Type Assignment of Review Articles in Web of Science, Scopus and Journals' Website

Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS) and Scopus is important. This study aims to investigate the document type assignation of review articles in web of Science, Scopus and Journals' website in a large scale. 27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed. The document types of these papers labeled on journals' website, and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons of wrongly assigning. For the document type labeled on the website, we further differentiate them into explicit review and implicit review based on whether the website directly indicating it is review or not. We find that WoS and Scopus performed similarly, with an average precision of about 99% and recall of about 80%. However, there were some differences between WoS and Scopus across different journal series and within the same journal series. The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly. This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus, and the identified pattern for assigning implicit reviews may be helpful to better labeling on website, WoS and Scopus

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments

Being there and being with them: the effects of visibility affordance of online short fitness video on users’ intention to cloud fitness

IntroductionCloud fitness is transforming indoor exercise for young people in China. Recent studies have explored the correlation between media use and health-promoting behavior by examining the motivation of individuals and the credibility of influencers. However, the role of media affordance has thus far been largely overlooked. Drawing on the theory of Stimulus-Organism-Response (SOR), the study investigated the indirect effect of visibility affordance on the intention to exercise with fitness influencers in the context of cloud fitness through psychological variables.MethodsThis paper, based on the online survey data (N = 456), analyses the effect of visibility affordance on the intention to fitness following with influencers. A moderated parallel mediation model was constructed to examine the relationship among related variables.ResultsThe paper draws the following conclusions: (1) Visibility affordance is positively related to the intention to exercise with fitness influencers. (2) Both the sense of social presence and immersion positively mediate the relationship between visibility affordance and the intention to exercise with fitness influencers. (3) The perceived popularity of the influencer positively moderates the relationship between social presence and the intention to exercise with fitness influencers and moderates the mediating role of social presence.DiscussionConsequently, this study enhances the existing body of knowledge in exercise behavior and health communication literature, and provides practical implications for short video platform, influencers and individuals in promoting healthier behaviors