重解个人信息的本质特征:算法识别性

个人信息的可识别定性引发了个人信息权与传统人格权的区分难题,司法裁判中既存在以传统人格权保护个人信息,也存在以个人信息权裁判传统人格权纠纷的现象。可识别不宜作为个人信息的本质特征,因为其脱离了个人信息制度产生的数字社会基础,忽视了数字社会与传统社会中的识别差异,混淆了个人信息在各部门法中的不同内涵。算法识别性才是个人信息的本质特征,个人信息权的客体范围、法律属性、权利内容等均产生于算法识别性。正是根据个人信息之算法识别性,方能把传统人格权与个人信息权案件至少在民法领域中区分开来。当信息处理方式在事实层面难以查明时,仍应归入传统人格权侵权案件;当算法技术程度不同时,需根据个案衡量能否适用个人信息权。尽管文章是以个人信息权与传统人格权的区分作为例证来揭示该定性的理论价值和实践意义,但个人信息算法识别性其实关涉数字空间中可计算个人信息所有法律制度的设计。

Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network

In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.

An early recognition algorithm for BitTorrent traffic based on improved K-means

In response to the deficiencies of BitTorrent, the concept of density radius was proposed, and the distance from the maximum point of radius density to cluster center as a cluster radius was taken to solve the too large cluster radius resulted from the discrete points and to reduce the false positive rate of early recognition algorithms. Simulation results show that in the actual network environment, the improved algorithm, compared with K-means, will reduce the false positive rate of early identification algorithm from 6.3% to 0.9% and has a higher operational efficiency.

A NEW LIKELIHOOD-BASED MODULATION CLASSIFICATION ALGORITHM USING MCMC

In this paper,a new likelihood-based method for classifying phase-amplitude-modulated signals in Additive White Gaussian Noise (AWGN) is proposed.The method introduces a new Markov Chain Monte Carlo (MCMC) algorithm,called the Adaptive Metropolis (AM) algorithm,to directly generate the samples of the target posterior distribution and implement the multidimensional integrals of likelihood function.Modulation classification is achieved along with joint estimation of unknown parameters by running an ergodic Markov Chain.Simulation results show that the proposed method has the advantages of high accuracy and robustness to phase and frequency offset.

Fracture property identification method based on shrinkage factor particle swarm optimization

In the multi-wave and multi-component seismic exploration,shear-wave will be split into fast wave and slow wave,when it propagates in anisotropic media. Then the authors can predict polarization direction and density of crack and detect the development status of cracks underground according to shear-wave splitting phenomenon. The technology plays an important role and shows great potential in crack reservoir detection. In this study,the improved particle swarm optimization algorithm based on shrinkage factor is combined with the Pearson correlation coefficient method to obtain the fracture azimuth angle and density. The experimental results show that the modified method can improve the convergence rate,accuracy,anti-noise performance and computational efficiency.

Progress billing method of accounting for long-term construction contracts

This article questions the reliability of the amount of revenue recognized in the percentage of completion （POC） method of revenue recognition in construction industry and recommends a new method based on the progress billing which is more reliable. The most commonly used method of revenue recognition in the construction industry is the percentage of completion method （POC）, where the revenue is recognized on the basis of the percentage of work completed. The calculation of percentage of work completed is made on the basis of the cost incurred for the contract work during the financial period and the cost required for completion of the work as estimated by the contractor. Here, the acceptance of the product by the buyer （contractee） is not involved in recognizing the revenue. The reliability of the amount of revenue and its collectability can be assured only when the buyer accepts the product. The approval of the progress bill by the contractee is needed to assure the reliability and collectability and it must be the event that triggers the recognition of revenue.