With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and...With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.展开更多
Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth d...Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.展开更多
The conception of truth-making,albeit in a rudimentary form,could already be discerned in the writings of G.E.Moore and E.Husserl in the early 1900s.A few years later it was more extensively exploited by William James...The conception of truth-making,albeit in a rudimentary form,could already be discerned in the writings of G.E.Moore and E.Husserl in the early 1900s.A few years later it was more extensively exploited by William James.It was Wittgenstein,however,who gave the concept a precise meaning.In 1913/1914 Wittgenstein advanced a theory of possible worlds,only one of which was real.Every proposition suggests a part of a possible world which does or does not correspond to parts of the real world.In the first case the proposition is true,in the second case false.Moreover,the part of the real world makes the sentence true.This part is a truth-maker,and the sentence is a truth-bearer.Surprisingly enough,Wittgenstein’s concept of truth-making had its family resemblance with William James’s conception of truth.In 1915 Wittgenstein stopped using the concept of truth-making-it was also not mentioned in the Tractatus.Unfortunately,Russell did not notice this and in 1918 he adopted the concept of truth-making.In the 1930s,it was used by some second generation analytic philosophers(Schlick,Stebbing,and Wisdom).However,it became rather popular among analytic philosophers only in the 1980s.展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.展开更多
基金Fundamental Research Funds for the Central Universities,China (No. 22D111207)。
文摘With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.
文摘Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.
文摘The conception of truth-making,albeit in a rudimentary form,could already be discerned in the writings of G.E.Moore and E.Husserl in the early 1900s.A few years later it was more extensively exploited by William James.It was Wittgenstein,however,who gave the concept a precise meaning.In 1913/1914 Wittgenstein advanced a theory of possible worlds,only one of which was real.Every proposition suggests a part of a possible world which does or does not correspond to parts of the real world.In the first case the proposition is true,in the second case false.Moreover,the part of the real world makes the sentence true.This part is a truth-maker,and the sentence is a truth-bearer.Surprisingly enough,Wittgenstein’s concept of truth-making had its family resemblance with William James’s conception of truth.In 1915 Wittgenstein stopped using the concept of truth-making-it was also not mentioned in the Tractatus.Unfortunately,Russell did not notice this and in 1918 he adopted the concept of truth-making.In the 1930s,it was used by some second generation analytic philosophers(Schlick,Stebbing,and Wisdom).However,it became rather popular among analytic philosophers only in the 1980s.
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
