Under the guidance of “technical value theory” taking both the natural and social attributes of technique into consideration, the fault in indirect infringements of copyrights by video sharing websites includes two ...Under the guidance of “technical value theory” taking both the natural and social attributes of technique into consideration, the fault in indirect infringements of copyrights by video sharing websites includes two forms:“intention” and “negligence”. As an objective criterion of negligence identification, the duty of care, is the natural extension of the security obligation in cyberspace;for a video sharing website, the foreseeable obligation of infringements is the main content of the duty of care;and it is highlighted that the degree of the duty of care hinges on different factors. For the form of liability, a video sharing website faces the difficulties of excessive costs in debt recovery after assuming the complementary liability, joint and several liability is thus alienated as an aggravating responsibility. However, according to the causative potency between the fault of a video sharing website and the infringement results, the several/shared liability can avoid the overburden to a video sharing website and distribute the risks of inadequate compensation based on the principle of fairness.展开更多
The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interest...The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.展开更多
Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After cons...Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.展开更多
Internet video is a video service that can be uploaded on the Internet and played online. As online video post-production of the key aspects related to the editing work is often the final stage of artistic effects, th...Internet video is a video service that can be uploaded on the Internet and played online. As online video post-production of the key aspects related to the editing work is often the final stage of artistic effects, the acquisition of a certain amount of online video editing skills is important. It will make people relatively independent of the picture and sound that mixed together organically, and be more conducive to the recognition of formation system, the color of the web video and audio clips.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
With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between busine...With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.展开更多
Despite that existing data sharing systems in online social networks （OSNs） propose to encrypt data before sharing, the multiparty access control of encrypted data has become a challenging issue. In this paper, we p...Despite that existing data sharing systems in online social networks （OSNs） propose to encrypt data before sharing, the multiparty access control of encrypted data has become a challenging issue. In this paper, we propose a secure data sharing scheme in 0SNs based on ciphertext-policy attribute- based proxy re-encryption and secret sharing. In order to protect users＇ sensitive data, our scheme allows users to customize access policies of their data and then outsource encrypted data to the OSNs service provider. Our scheme presents a multiparty access control model, which enables the disseminator to update the access policy of ciphertext if their attributes satisfy the existing access policy. Further, we present a partial decryption construction in which the computation overhead of user is largely reduced by delegating most of the decryption operations to the OSNs service provider. We also provide checkability on the results returned from the OSNs service provider to guarantee the correctness of partial decrypted ciphertext. Moreover, our scheme presents an efficient attribute revocation method that achieves both forward and backward secrecy. The security and performance analysis results indicate that the proposed scheme is secure and efficient in OSNs.展开更多
基金Marxism Theoretical Research and Construction Project and National Social Science Fund Project “Research on Intellectual Property Protection and Innovative Development”(2016MZD022)
文摘Under the guidance of “technical value theory” taking both the natural and social attributes of technique into consideration, the fault in indirect infringements of copyrights by video sharing websites includes two forms:“intention” and “negligence”. As an objective criterion of negligence identification, the duty of care, is the natural extension of the security obligation in cyberspace;for a video sharing website, the foreseeable obligation of infringements is the main content of the duty of care;and it is highlighted that the degree of the duty of care hinges on different factors. For the form of liability, a video sharing website faces the difficulties of excessive costs in debt recovery after assuming the complementary liability, joint and several liability is thus alienated as an aggravating responsibility. However, according to the causative potency between the fault of a video sharing website and the infringement results, the several/shared liability can avoid the overburden to a video sharing website and distribute the risks of inadequate compensation based on the principle of fairness.
文摘The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.
文摘Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.
文摘Internet video is a video service that can be uploaded on the Internet and played online. As online video post-production of the key aspects related to the editing work is often the final stage of artistic effects, the acquisition of a certain amount of online video editing skills is important. It will make people relatively independent of the picture and sound that mixed together organically, and be more conducive to the recognition of formation system, the color of the web video and audio clips.
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With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.
基金This work has been supported by the National Natural Science Foundation of China under Grant No.61272519,the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20120005110017,and the National Key Technology R&D Program under Grant No.2012BAH06B02
文摘Despite that existing data sharing systems in online social networks （OSNs） propose to encrypt data before sharing, the multiparty access control of encrypted data has become a challenging issue. In this paper, we propose a secure data sharing scheme in 0SNs based on ciphertext-policy attribute- based proxy re-encryption and secret sharing. In order to protect users＇ sensitive data, our scheme allows users to customize access policies of their data and then outsource encrypted data to the OSNs service provider. Our scheme presents a multiparty access control model, which enables the disseminator to update the access policy of ciphertext if their attributes satisfy the existing access policy. Further, we present a partial decryption construction in which the computation overhead of user is largely reduced by delegating most of the decryption operations to the OSNs service provider. We also provide checkability on the results returned from the OSNs service provider to guarantee the correctness of partial decrypted ciphertext. Moreover, our scheme presents an efficient attribute revocation method that achieves both forward and backward secrecy. The security and performance analysis results indicate that the proposed scheme is secure and efficient in OSNs.
