Qun Jin
Biographic Data
| ID | 7744967 |
|---|---|
| NAME | Qun Jin |
| GIVEN NAMES | Qun |
| FAMILY NAME | Jin |
| SIGNATURE | JIN Q |
| AFFILIATIONS | Waseda University |
| ORCID | 0000-0003-4844-6995 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Identification and quantification of approval desire in social networking service posts and analysis of their linguistic features
In recent years, the analysis of approval desire in social networking services (SNSs) has garnered significant research attention. However, identifying specific words that influence the perception of approval desire remains challenging. Additionally, limited research has objectively examined whether posts evoke approval desire from the readers' perspective. This paper introduces a framework for identifying and quantifying approval desire in SNS p…
Joint association of antioxidant intakes from diet and supplements and sedentary behavior with all-cause and cardiovascular disease mortality among US adults
Participants with both high-antioxidant diet and nonprolonged sedentary behavior had the lowest all-cause and CVD mortality. Additionally, nonprolonged sedentary behavior can help counteract the harms of low-antioxidant diet, whereas a high-antioxidant diet fails to offset the deleterious effect of prolonged sedentary behavior
LASGRec
With the explosion of information, personalized recommender plays a vital role in almost all economic platforms. Usually, the recommender exploits user–item (UI) interactive data to learn users’ latent interests, and then correspondingly conducts recommendations. To address the problem of sparse interactions, graph neural networks (GNNs) have been used to efficiently learn the latent representations of users and items, through structurally modeli…
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications
The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning p…
Research and Implementation of Chinese Couplet Generation System With Attention-Based Transformer Mechanism
Couplet is a unique art form in Chinese traditional culture. The development of deep neural network (DNN) technology makes it possible for computers to automatically generate couplets. Especially, Transformer is a DNN-based “Encoder–Decoder” framework, and widely used in natural language processing (NLP). However, the existed Transformer mechanism cannot fully exploit the essential linguistic knowledge in Chinese, including the special format and…
Measurement and quantification of an individual's feelings for a place in personal data analysis
Location-related data are an important subset of personal data. An individual may have a positive or negative feeling for a specific place, which is important for personal data analysis. There are many studies on sentiment analysis within text data, such as tweets, but few studies have been conducted specifically on an individual's feelings regarding locations. In this study, we focus on measuring and quantifying an individual's feelings for a pl…
DNN-Dp
Deep neural network (DNN) learning has witnessed significant applications in various fields, especially for prediction and classification. Frequently, the data used for training are provided by crowdsourcing workers, and the training process may violate their privacy. A qualified prediction model should protect the data privacy in training and classification/prediction phases. To address this issue, we develop a differential privacy (DP)-enabled …
Computer Supported Social Networking For Augmenting Cooperation
No prominent works on this page.
Computer Supported Social Networking For Augmenting Cooperation
DNN-Dp
Deep neural network (DNN) learning has witnessed significant applications in various fields, especially for prediction and classification. Frequently, the data used for training are provided by crowdsourcing workers, and the training process may violate their privacy. A qualified prediction model should protect the data privacy in training and classification/prediction phases. To address this issue, we develop a differential privacy (DP)-enabled …
Measurement and quantification of an individual's feelings for a place in personal data analysis
Location-related data are an important subset of personal data. An individual may have a positive or negative feeling for a specific place, which is important for personal data analysis. There are many studies on sentiment analysis within text data, such as tweets, but few studies have been conducted specifically on an individual's feelings regarding locations. In this study, we focus on measuring and quantifying an individual's feelings for a pl…
Research and Implementation of Chinese Couplet Generation System With Attention-Based Transformer Mechanism
Couplet is a unique art form in Chinese traditional culture. The development of deep neural network (DNN) technology makes it possible for computers to automatically generate couplets. Especially, Transformer is a DNN-based “Encoder–Decoder” framework, and widely used in natural language processing (NLP). However, the existed Transformer mechanism cannot fully exploit the essential linguistic knowledge in Chinese, including the special format and…
Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications
The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning p…
LASGRec
With the explosion of information, personalized recommender plays a vital role in almost all economic platforms. Usually, the recommender exploits user–item (UI) interactive data to learn users’ latent interests, and then correspondingly conducts recommendations. To address the problem of sparse interactions, graph neural networks (GNNs) have been used to efficiently learn the latent representations of users and items, through structurally modeli…
Joint association of antioxidant intakes from diet and supplements and sedentary behavior with all-cause and cardiovascular disease mortality among US adults
Participants with both high-antioxidant diet and nonprolonged sedentary behavior had the lowest all-cause and CVD mortality. Additionally, nonprolonged sedentary behavior can help counteract the harms of low-antioxidant diet, whereas a high-antioxidant diet fails to offset the deleterious effect of prolonged sedentary behavior
Identification and quantification of approval desire in social networking service posts and analysis of their linguistic features
In recent years, the analysis of approval desire in social networking services (SNSs) has garnered significant research attention. However, identifying specific words that influence the perception of approval desire remains challenging. Additionally, limited research has objectively examined whether posts evoke approval desire from the readers' perspective. This paper introduces a framework for identifying and quantifying approval desire in SNS p…
Computer Science (6 works) · Artificial Intelligence (5 works) · Machine learning (3 works) · Artificial neural network (2 works) · Data mining (2 works) · Data set (2 works) · Human Mobility and Location-Based Analysis (2 works) · Privacy-Preserving Technologies in Data (2 works) · Sentiment Analysis and Opinion Mining (2 works) · Advanced Graph Neural Networks (1 work)