Popularity Prediction of Social Media Post Using Tensor Factorization

The traditional method of doing business has been disrupted by socialmedia. In order to develop the enterprise, it is essential to forecast the level ofinteraction that a new post would receive from social media users. It is possiblefor the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detectionstrategies that are user-based or population-based are unable to keep up with theseshifts, which leads to inaccurate forecasts. This work makes a prediction abouthow popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that istensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation bychoosing the top contents that were most like each other. The tensor is factorisedwith the application of the Adam optimization. It has been modified such that thebias is now included in the gradient function of A-PARAFAC, and the value ofthe bias is updated after each iteration. The prediction accuracy is improved by32.25% with this strategy compared to other state of the art methods.

Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems.Time series,content and meta-feature are important features in news popularity prediction.However,there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance.This work proposes a novel deep learning model named Multiple Features Dynamic Fusion(MFDF)for news popularity prediction.For modeling time series,long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity.The typical convolution neural network gets headline semantic representation for modeling news headlines.In addition,a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement.A factorization machine is employed to model the interaction relationship between metafeatures.Considering the role of these features at different stages,the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically.During the training phase,thiswork designs a loss function based on Newton’s cooling law to train the model better.Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.

Chinese Outbound Tour Groups Enjoying Popularity in Singapore

The arrival of the Chinese tour group in Singapore with the sincere friendship between China and Singapore after the outbreak of COVID-19 marked the comprehensive recovery of people-to-people and cultural exchanges between the two countries and the new start of China-Singapore cooperation after three years of development.

Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

With the rapid development of networks,users are increasingly seeking richer and high-quality content experience,and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching.Therefore,it will be an effective solution to combine content popularity prediction based on machine learning(ML)and content caching to enable the network to predict and analyze popular content.However,the data sets which contain users’private data cause the risk of privacy leakage.In this paper,to address this challenge,we propose a privacy-preserving algorithm based on federated learning(FL)and long short-term memory(LSTM),which is referred to as FL-LSTM,to predict content popularity.Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection.Meanwhile,the caching policy in this paper raises about 14.3%of the content hit rate.

Growing by Leaps and Bounds Chinese wushu have gained more popularity and broader adoption in Africa

Wushu,or Chinese martial arts,is set to make its Olympic debut in the Youth Olympic Games 2026 in Dakar,the first Olympic competition to be staged in Africa.Wushu,which featured at the All-Africa Youth Games in 2018,has gone through a tortuous process in being accepted as an Olympic sport event.The International Wushu Federation(IWUF)first submitted applications for inclusion to the International Olympic Committee in 2001,followed by two more failed bids in 2008 and 2011.Finally,it tasted success in Africa,which is also a testament to its popularity in the continent.

Investigation and analysis of the status of cancer health popularization in China,2023

BACKGROUND Cancer presents a significant public health challenge in China,necessitating broad collaboration across society.The Chinese government has articulated a goal to increase the overall five-year survival rate for cancer by 15%by 2030.Achieving this objective requires not only advances in medical technology,but also an im-provement in the dissemination of knowledge pertaining to cancer prevention and treatment.AIM To provide a comprehensive understanding of the status of cancer prevention and level of popularization in China in 2023.METHODS From January 2023 to May 2023,online questionnaires were distributed to 3000 participants,including medical personnel,patients with cancer,their families,and the general public.There were 2711 valid responses,covering the entire nation.RESULTS A total of 1020 medical personnel and 1691 patients with cancer,their family members,and the general public participated in the survey.Among medical personnel,93.2%had popularized cancer health.Commonly addressed topics included cancer prevention(85.9%)and cancer screening(77.8%).Primary challenges included time constraints(73.9%),insufficient personnel and material support(66.7%),and uncertainty as to where to begin(49.3%).Among patients with cancer,their family members,and the general public,93.4%reported reading or watching cancer science popularization materials and 56.9%expressed a desire for deeper understanding.The most sought-after topics in cancer science popularization included cancer screening(80.2%)and cancer prevention(75.8%).The greatest challenge encountered in accessing cancer health popularization was an abundance of misinformation(67.5%).CONCLUSION Most clinical doctors,patients,family,and the general public wish to participate in cancer education.However,improvement in the quality of content in cancer prevention and treatment education is required.

National Medical Products Administration Releasing Popular Science Article about Using Sunscreen“Spray”Cosmetics Safely

On Apr.15th,2024,National Medical Products Administration released a popular science article about using sunscreen“spray”cosmetics safely,reminding consumers to care about how to use sunscreen“spray”cosmetics correctly.According to the article,common sunscreen“spray”in the market can be divided into spray type and aerosol type,and incorrect use of them will cause the risk of inhalation or flammability.To use sunscreen“spray”cosmetics correctly,one should try to avoid inhaling“spray”;put the sunscreen“spray”in a cool,dry and ventilated environment not exposed to direct sunlight,and avoid using them on wounds,redness&swelling,eczema or other abnormal places of the skin.

The Research on the Paths of Popular Science Education about Myopia Prevention and Control among College Students in the Context of Shifting the Focus Forward

Currently, the number of myopic people in China has reached as high as 600 million, and it shows a trend of “early onset, high incidence, and a high degree of severity”. The issue of myopia among children and adolescents has attracted widespread attention from all sectors of society. Comprehensive myopia prevention and control efforts have been launched nationwide. Popular science education, vision screening, and improving visual environments are effective measures for preventing myopia. However, among the target audience for comprehensive myopia prevention and control, the preschool children group has been neglected, and the work of myopia prevention and control for this group has been significantly weakened and marginalized. Due to insufficient awareness of prevention and control, the hyperopia reserve in the preschool children group is being continuously depleted, leading to the early onset of myopia. In light of this situation, the focus of myopia prevention and control needs to shift forward, and it is worth exploring and pondering how college students with a background in optometry can develop entertaining and engaging popular science education materials and methods.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

On-line Popularity Monitoring Method Based on Bloom Filters and Hash tables for Differentiated Traffic

Towards line speed and accurateness on-line content popularity monitoring on Content Centric Networking(CCN) routers, we propose a three-stage scheme based on Bloom filters and hash tables for differentiated traffic. At the first stage, we decide whether to deliver the content to the next stage depending on traffic types. The second stage consisting of Standard Bloom filters(SBF) and Counting Bloom filters(CBF) identifies the popular content. Meanwhile, a scalable sliding time window based monitoring scheme for different traffic types is proposed to implement frequent and real-time updates by the change of popularities. Hash tables according with sliding window are used to record the popularity at the third stage. Simulation results reveal that this method reaches a 40 Gbps processing speed at lower error probability with less memory, and it is more sensitive to the change of popularity. Additionally, the architecture which can be implemented in CCN router is flexible and scalable.

The Realization of Popularity in the Geopark Interpretation System

By analyzing the existing research achievements on the geopark interpretation system,this article has brought up a new explanation of the system from the aspect of easy-understood principle.We try to present our tourists with geoheritage,natural and cultural resources by various medium,so that people could know more about geosciences during the tour.In the end,geoheritage protection is enhanced by such

Pet Economy Gains Popularity

With some improvements in the consumption structure,a steady increase in per capita disposable income,the growing number of urban youngsters living alone,the trend towards an increasingly aged population and so many other factors,raising pets has become one of the most promoted lifestyles.As people spend more money on their pets every year,many industries including pet food,products,medical care and pet services are developing,and a complete consumption chain was gradually formed,thus leading to a thriving pet economy.

Exploring Content Popularity in Information-Centric Networks

Information-Centric Networking(ICN), an alternative architecture to the current Internet infrastructure, focuses on the distribution and retrieval of content by employing caches in a network to reduce network traffic. The employment of caches may be accomplished using graph-based and content-based criteria such as the position of a node in a network and content popularity. The contribution of this paper lies on the characterization of content popularity for on-path in-network caching. To this end, four dynamic approaches for identifying content popularity are evaluated via simulations. Content popularity may be determined per chunk or per object, calculated by the number of requests for a content against the sum of requests or the maximum number of requests. Based on the results, chunk-based approaches provide 23% more accurate content popularity calculations than object-based approaches. In addition, approaches that are based on the comparison of a content against the maximum number of requests have been shown to be more accurate than the alternatives.

Popularity, likeability, and risk-taking in middle adolescence

This study investigated the roles of adolescent popularity and likeability in eight domains of risk-taking in Australian grade 9 students (53% girls). The eight domains included previously examined areas of aggressive behaviours, alcohol use, and sexual intercourse, and areas where there is scarce information, including antisocial activities, unprotected intercourse, body image-related risk-taking, unsafe road practices, and stranger-related risk-taking. The results indicated a clear association between popularity and higher risk-taking in five of the eight domains. This is contrasted with likeability, which was not directly related to risk-taking aside from one two-way interaction with gender for sexual intercourse. The findings demonstrate the importance of including a broader range of risk-taking activities when considering popularity, particularly stranger-related risk-taking.

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

Considering the unmanned aerial vehicle(UAV) three-dimensional(3D) posture, a novel 3D non-stationary geometry-based stochastic model(GBSM) is proposed for multiple-input multipleoutput(MIMO) UAV-to-vehicle(U2V) channels. It consists of a line-of-sight(Lo S) and non-line-of-sight(NLo S) components. The factor of fuselage posture is considered by introducing a time-variant 3D posture matrix. Some important statistical properties, i.e.the temporal autocorrelation function(ACF) and spatial cross correlation function(CCF), are derived and investigated. Simulation results show that the fuselage posture has significant impact on the U2V channel characteristic and aggravate the non-stationarity. The agreements between analytical, simulated, and measured results verify the correctness of proposed model and derivations. Moreover, it is demonstrated that the proposed model is also compatible to the existing GBSM without considering fuselage posture.

A Measurement Study on Resource Popularity and Swarm Evolution of BitTorrent System

Analyzing and modeling of the BitTorrent (BT) resource popularity and swarm evolution is important for better understanding current BT system and designing accurate BT simulators. Although lots of measurement studies on BT almost cover each important aspect, little work reflects the recent development of BT system. In this paper, we develop a hybrid measurement system incorporating both active and passive approaches. By exploiting DHT (Distribute Hash Table) and PEX (Peer Exchange) protocols, we collect more extensive information compared to prior measurement systems. Based on the measurement results, we study the resource popularity and swarm evolution with different population in minute/ hour/day scales, and discover that: 1) the resources in BT system appear obvious unbalanced distribution and hotspot phenomenon, in that 74.6% torrents have no more than 1000 peers;2) The lifetime of torrents can be divided into a fast growing stage, a dramatically shrinking stage, a sustaining stage and a slowly fading out stage in terms of swarm population;3) Users’ interest and diurnal periodicity are the main factors that influence the swarm evolution. The former dominates the first two stages, while the latter is decisive in the third stage. We raise an improved peer arrival rate model to describe the variation of the swarm population. Comparison results show that our model outperforms the state-of-the-art approach according to root mean square error and correlation coefficient.

New Energy Vehicles Are Gaining Great Popularity

China sold 334,000 new energy ve­hicles in February,up 184.3%year-on-year,according to information released by the China Association of Automobile Manufacturers on March 11.The cumulative sales volume from January to Febru­ary was 765,000 units,up 154.7%year on year.The China Association of Automobile Manu­facturers pointed out that from the automobile de­velopment trend in the first quarter,a series of poli­cies have been unveiled with more favorable tax and fee cuts to help relieve the burden of enterprises,and the construction of large-scale infrastructure proj­ects have been started since the first quarter,all of which play a significant role in promoting economic growth.At the same time,auto companies are also accelerating the pace at which they are bringing new products on to the market,and the auto market is expected to show steady growth in the first quarter.

Modeling and Predicting of News Popularity in Social Media Sources

The popularity of news,which conveys newsworthy events which occur during day to people,is substantially important for the spectator or audience.People interact with news website and share news links or their opinions.This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources.These techniques consist of basically two phrases:a)the training data is sent as input to the classifier algorithm,b)the performance of prelearned algorithm is tested on the testing data.And so,a knowledge discovery from the data is performed.In this context,firstly,twelve datasets from a set of data are obtained within the frame of four categories:Economic,Microsoft,Obama and Palestine.Second,news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees,Multi-Layer Perceptron and Random Forest learning algorithms.The prediction performances of all algorithms are examined by considering Mean Absolute Error,Root Mean Squared Error and the R-squared evaluation metrics.The results show that most of the models designed by using these algorithms are proved to be applicable for this subject.Consequently,a comprehensive study for the news prediction is presented,using different techniques,drawing conclusions about the performances of algorithms in this study.

A prediction mechanism of file access popularity and optimal strategy of replicas deployment in cloud computing

In cloud computing,the number of replicas and deployment strategy have extensive impacts on user's requirement and storage efficiency.Therefore,in this paper,a new definition of file access popularity according to users' preferences,and its prediction algorithm are provided to predict file access trend with historical data.Files are sorted by priority depending on their popularity.A mathematical model between file access popularity and the number of replicas is built so that the reliability is increased efficiently.Most importantly,we present an optimal strategy of dynamic replicas deployment based on the file access popularity strategy with the overall concern of nodes' performance and load condition.By this strategy,files with high priority will be deployed on nodes with better performance therefore higher quality of service is guaranteed.The strategy is realized in the Hadoop platform.Performance is compared with that of default strategy in Hadoop and CDRM strategy.The result shows that the proposed strategy can not only maintain the system load balance,but also supply better service performance,which is consistent with the theoretical analysis.

Laos:‘Happy Chinese New Year’Online Temple Fair Inspiring Its Popularity

In Laos,the series of cultural events 2022"Happy Chinese New Year",in the theme of watching the Winter Olympics and celebrating the New Year,was continuously launched with a strong momentum for more than a month around the Spring Festival,attracting broad attention and warm welcome of the Laos people and the media of China and Laos.Among them,"Happy Chinese New Year"Online Temple Fair really inspired its popularity in Laos.