While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a sh...While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a short‑video platform with her husband Han Peng,their ostrich farming business has flourished,garnering nearly 3 million fans on the platform.“The highest viewership for one single live broadcast reached over 6 million,”said Wang,adding that riding an ostrich at high speed was an essential part of her broadcasts.Through her videos,Wang brought fans close to the ostriches,providing details of their habits,farming methods and economic value.展开更多
You may not expect that the talk shows that frequently dominate the charts today originated from coffee shops in England in the 18th century.The coffee shop was a breeding place for early public discussions,where peop...You may not expect that the talk shows that frequently dominate the charts today originated from coffee shops in England in the 18th century.The coffee shop was a breeding place for early public discussions,where people engaged in free debates on political and social issues.展开更多
If you are asked what requires you to win a lottery and costs RMB 168,it may not be a car license plate,but a fridge magnet.Collecting fridge magnets has quietly become a popular culture in recent years.Many young peo...If you are asked what requires you to win a lottery and costs RMB 168,it may not be a car license plate,but a fridge magnet.Collecting fridge magnets has quietly become a popular culture in recent years.Many young people have more demand for fridge magnets than for refrigerators.The empty refrigerator covered with fridge magnets has become a trend.People with no idea how to choose souvenirs regard the fridge magnets as the best gifts,allowing this seemingly inconspicuous small product to create a billion-yuan market.展开更多
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. ...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.展开更多
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 manageme...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.展开更多
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 touri...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展开更多
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....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.展开更多
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...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.展开更多
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 o...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.展开更多
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...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.展开更多
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 ...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.展开更多
文摘While feeding her ostriches,Wang Xue swiftly responds to fans'questions with a sense of humor through her popular live‑streaming channel.Since Wang,30,an expert in ostrich farming,started live broadcasting on a short‑video platform with her husband Han Peng,their ostrich farming business has flourished,garnering nearly 3 million fans on the platform.“The highest viewership for one single live broadcast reached over 6 million,”said Wang,adding that riding an ostrich at high speed was an essential part of her broadcasts.Through her videos,Wang brought fans close to the ostriches,providing details of their habits,farming methods and economic value.
文摘You may not expect that the talk shows that frequently dominate the charts today originated from coffee shops in England in the 18th century.The coffee shop was a breeding place for early public discussions,where people engaged in free debates on political and social issues.
文摘If you are asked what requires you to win a lottery and costs RMB 168,it may not be a car license plate,but a fridge magnet.Collecting fridge magnets has quietly become a popular culture in recent years.Many young people have more demand for fridge magnets than for refrigerators.The empty refrigerator covered with fridge magnets has become a trend.People with no idea how to choose souvenirs regard the fridge magnets as the best gifts,allowing this seemingly inconspicuous small product to create a billion-yuan market.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No.61521003)the National Basic Research Program of China (2012CB315901, 2013CB329104)+1 种基金the National Natural Science Foundation of China (Grant No. 61372121, 61309019, 61309020)the National HighTech Research & Development Program of China (Grant No. 2015AA016102, 2013AA013505)
文摘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.
文摘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.
文摘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
基金funded by the Higher Education Authority (HEA)co-funded under the European Regional Development Fund (ERDF)
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
