Applied Sciences

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31 pages, 3222 KiB

Open AccessArticle

Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game

by Hongbin Hu, Yongbin Wang, Guohui Song, Weijian Fan and Chenming Liu

Appl. Sci. 2023, 13(18), 10089; https://doi.org/10.3390/app131810089 - 07 Sep 2023

Cited by 1 | Viewed by 921

As a new factor of production, data element has profoundly changed our mode of production, lifestyle and social governance style. The sharing of a data element in the convergence media ecology can greatly improve the circulation of a data element and enhance the [...] Read more.

As a new factor of production, data element has profoundly changed our mode of production, lifestyle and social governance style. The sharing of a data element in the convergence media ecology can greatly improve the circulation of a data element and enhance the value of a data element; however, it may face problems such as insufficient sharing willingness, incomplete sharing circulation mechanism and inadequate implementation of the incentive mechanism. To solve these problems, this paper introduced the evolutionary game theory in the convergence media ecology and established the data-sharing model according to the characteristics of nodes. We analyzed the ecological node evolution path, evolutionary stable strategy and the corresponding state conditions in the model. Furthermore, we carried out the sampling experiment simulation, which verified the effectiveness of the research content in this paper. At the end of the article, we summarize and give some key factors to increase the willingness to participate in sharing in convergence media ecology. This paper enriched the research field of data element sharing in convergence media and explored the willingness and tendency of the participants. The research results can provide targeted suggestions for promoting the sharing of data elements in convergence media ecology. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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22 pages, 4685 KiB

Open AccessArticle

Value Assessment of UGC Short Videos through Element Mining and Data Analysis

by Jinyu Fang, Yuan Ni and Jian Zhang

Appl. Sci. 2023, 13(16), 9418; https://doi.org/10.3390/app13169418 - 19 Aug 2023

Cited by 1 | Viewed by 1273

Abstract

UGC short videos play a crucial role in sharing information and disseminating content in the era of new information technology. Accurately assessing the value of UGC short videos is highly significant for the sustainable development of self-media platforms and the secure governance of [...] Read more.

UGC short videos play a crucial role in sharing information and disseminating content in the era of new information technology. Accurately assessing the value of UGC short videos is highly significant for the sustainable development of self-media platforms and the secure governance of cyberspace. This study proposes a method for assessing the value of UGC short videos from the perspective of element mining and data analysis. The method involves three steps. Firstly, the text clustering algorithm and topic mapping visualization technology are utilized to identify elements for assessing the value of UGC short videos and construct an assessment index system. Secondly, structured data indexes are quantified using platform data statistics, while unstructured data indexes are quantified using the LSTM fine-grained sentiment analysis model. Lastly, the VIKOR model, incorporating an improved gray correlation coefficient, is employed to effectively evaluate the value of UGC short videos. The empirical results indicate that the value of current domestic UGC short videos is primarily associated with three dimensions: the creators, the platforms, and the users. It encompasses 11 value elements, including fan popularity, economic returns of creation, and frequency of interaction. Additionally, we assess the value of short videos within the mainstream partitions of the Bilibili platform and generate a value radar chart. Our findings reveal that short videos in game partitions generate higher revenue for creators and platforms but may neglect users’ needs for knowledge, culture, and other content. Conversely, short videos in the knowledge, food, and music partitions demonstrate specific distinctions in fulfilling users’ requirements. Ultimately, we offer personalized recommendations for the future development of high-value UGC short videos within the mainstream partitions. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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11 pages, 377 KiB

Open AccessArticle

GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization

by Runze Mao, Wenqi Fan and Qing Li

Appl. Sci. 2023, 13(16), 9166; https://doi.org/10.3390/app13169166 - 11 Aug 2023

Viewed by 881

Abstract

Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most [...] Read more.

Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most knowledge, its capability in treating different node subgroups fairly during compression remains unexplored. In this paper, we investigate current graph condensation techniques from a perspective of fairness, and show that they bear severe disparate impact toward node subgroups. Specifically, GNNs trained on condensed graphs become more biased than those trained on original graphs. Since the condensed graphs comprise synthetic nodes, which are absent of explicit group IDs, the current algorithms used to train fair GNNs fail in this case. To address this issue, we propose Graph Condensation with Adversarial Regularization (GCARe), which is a method that directly regularizes the condensation process to distill the knowledge of different subgroups fairly into resulting graphs. A comprehensive series of experiments substantiated that our method enhances the fairness in condensed graphs without compromising accuracy, thus yielding more equitable GNN models. Additionally, our discoveries underscore the significance of incorporating fairness considerations in data condensation, and offer invaluable guidance for future inquiries in this domain. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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10 pages, 3885 KiB

Open AccessCommunication

Smoothness Harmonic: A Graph-Based Approach to Reveal Spatiotemporal Patterns of Cortical Dynamics in fMRI Data

by Wenjun Bai

Appl. Sci. 2023, 13(12), 7130; https://doi.org/10.3390/app13127130 - 14 Jun 2023

Viewed by 813

Abstract

Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness [...] Read more.

Despite fMRI data being interpreted as time-varying graphs in graph analysis, there has been more emphasis on learning sophisticated node embeddings and complex graph structures rather than providing a macroscopic description of cortical dynamics. In this paper, I introduce the notion of smoothness harmonics to capture the slowly varying cortical dynamics in graph-based fMRI data in the form of spatiotemporal smoothness patterns. These smoothness harmonics are rooted in the eigendecomposition of graph Laplacians, which reveal how low-frequency-dominated fMRI signals propagate across the cortex and through time. We showcase their usage in a real fMRI dataset to differentiate the cortical dynamics of children and adults while also demonstrating their empirical merit over the static functional connectomes in inter-subject and between-group classification analyses. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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16 pages, 3451 KiB

Open AccessArticle

GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

by Chenyang Qi, Jiaying Ren and Jin Su

Appl. Sci. 2023, 13(12), 7104; https://doi.org/10.3390/app13127104 - 14 Jun 2023

Cited by 8 | Viewed by 1436

Abstract

Stock indices are considered to be an important indicator of financial market volatility in various countries. Therefore, the stock market forecast is one of the challenging issues to decrease the uncertainty of the future direction of financial markets. In recent years, many scholars [...] Read more.

Stock indices are considered to be an important indicator of financial market volatility in various countries. Therefore, the stock market forecast is one of the challenging issues to decrease the uncertainty of the future direction of financial markets. In recent years, many scholars attempted to use different conventional statistical and deep learning methods to predict stock indices. However, the non-linear financial noise data will usually cause stochastic deterioration and time lag in forecast results, resulting in existing neural networks that do not demonstrate good prediction results. For this reason, we propose a novel framework to combine the gated recurrent unit (GRU) neural network with the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to predict the stock indices with better accuracy, in which the wavelet threshold method is especially used to denoise high-frequency noises in the sub-signals to exclude noise interference for future data predictions. Firstly, we choose representative datasets collected from the closing prices of S&P500 and CSI 300 stock indices to evaluate the proposed GRU-CEEMDAN–wavelet model. Additionally, we compare the improved model to the traditional ARIMA and several modified neural network models using different gate structures. The result shows that the mean values of MSE and MAE for GRU based on CEEMDAN–wavelet are the smallest by significance analysis. Overall, we found that our model could improve prediction accuracy and alleviates the time lag problem. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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15 pages, 973 KiB

Open AccessArticle

Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China

by Ying Liu, Dickson K. W. Chiu and Kevin K. W. Ho

Appl. Sci. 2023, 13(6), 3386; https://doi.org/10.3390/app13063386 - 07 Mar 2023

Cited by 22 | Viewed by 4027

Abstract

Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka [...] Read more.

Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka TikTok worldwide), with social media analytics, including topic and correlation analysis. Results indicated that the number of fans of an account is positively correlated with the number of likes (p < 0.001) and independent of the number of videos (p > 0.05). Libraries post videos most often on the topic of “Reading Promotion”(31%), but the marketing performance on this topic is average (Mean DMI = 90.27). The most popular topics for patrons are “Hot Topics” and “Knowledge Quiz” (Mean DMI = 207.00). Using short-form videos for library marketing is a new practice, and scant studies have evaluated such performance, especially in Asia. Our results strengthen library practitioners’ awareness and reflections on conducting new media services and short-form video promotion, especially for the newer generation. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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17 pages, 5455 KiB

Open AccessArticle

Sentiment Analysis and Topic Modeling Regarding Online Classes on the Reddit Platform: Educators versus Learners

by Shanghao Li, Zerong Xie, Dickson K. W. Chiu and Kevin K. W. Ho

Appl. Sci. 2023, 13(4), 2250; https://doi.org/10.3390/app13042250 - 09 Feb 2023

Cited by 29 | Viewed by 3955

Abstract

The world is witnessing an unpredictable COVID-19 pandemic that has impacted all levels of online education, shaping future trends. However, this shift was so sudden and drastic that unrevealed puzzles exist regarding the public’s authentic opinion towards online classes, even though three years [...] Read more.

The world is witnessing an unpredictable COVID-19 pandemic that has impacted all levels of online education, shaping future trends. However, this shift was so sudden and drastic that unrevealed puzzles exist regarding the public’s authentic opinion towards online classes, even though three years have passed. Many experts and policymakers have conducted qualitative and quantitative research to explore effective pedagogies, the satisfaction of different stakeholders, and factors influential on learners’ performance. However, scant studies have examined personal opinions and concerns toward online classes hidden behind people’s anonymous posts on social media. This research investigates the sentiments, concerns, and their variance with time regarding online classes by learners and educators on Reddit, which is a dominant social network among them. Data were collected via the official API from identified relevant subreddits and keyword search results across Reddit. Sentiment analysis was applied to reveal their emotions and their changes. Topic modeling was conducted to discover the concerns hidden in the posts. The results revealed the concerns about online classes, such as severe cheating behaviors, and showed doubts about previous strategies to solve disadvantages in online classes. In addition, the results verified the habitual difference and motivations of social media usage between educators and learners. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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17 pages, 10502 KiB

Open AccessArticle

Automated Analysis of Open-Ended Students’ Feedback Using Sentiment, Emotion, and Cognition Classifications

by Melanie Fargues, Seifedine Kadry, Isah A. Lawal, Sahar Yassine and Hafiz Tayyab Rauf

Appl. Sci. 2023, 13(4), 2061; https://doi.org/10.3390/app13042061 - 05 Feb 2023

Cited by 1 | Viewed by 1399

Abstract

Students’ feedback is pertinent in measuring the quality of the educational process. For example, by applying lexicon-based sentiment analysis to students’ open-ended course feedback, we can detect not only their sentiment orientation (positive, negative, or neutral) but also their emotional valences, such as [...] Read more.

Students’ feedback is pertinent in measuring the quality of the educational process. For example, by applying lexicon-based sentiment analysis to students’ open-ended course feedback, we can detect not only their sentiment orientation (positive, negative, or neutral) but also their emotional valences, such as anger, anticipation, disgust, fear, joy, sadness, surprise, or trust. However, most currently used assessment tools cannot effectively measure emotional engagement, such as interest level, enjoyment, support, curiosity, and sense of belonging. Moreover, none of those tools utilize Bloom’s taxonomy for students’ learning-level assessment. In this work, we develop a user-friendly application based on NLP to help the teachers understand the students’ perception of their learning by analyzing their open-ended feedback. This allows us to examine the sentiment and the embedded emotions using a customized dictionary of emotions related to education. The application can also classify the students’ emotions according to Bloom’s taxonomy. We believe our application will help teachers improve their course delivery. Full article

(This article belongs to the Special Issue Advances in Data Science and Its Applications)

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