论网络法权构建中的主体性原则

大数据和人工智能技术凸显了法律人格边界模糊和结构性不平等加大等问题,须在主体性原则的理论层次上才能有效处理。主体性原则是现代法权体系构建的根本原则,具体体现为"自由原则"的核心原则,以及"理性原则"和"人文原则"两个基础原则。网络空间的发展,开显了更高层次的人类自由——"积极自由"的可能性路径,同时也面临"消极自由"受到侵害的现实威胁,二者构成自由理论的内在矛盾。网络空间的层次性,错开了"积极自由"和"消极自由"的直接对立;网络空间的开放性,要求确立网络隐私权在落实主体性原则方面的基础性地位;网络空间的系统性,要求强化对算法的审查和监管。

高等教育治理的法权网络探究

黑格尔的国家干预理论、法权理论和网络治理理论为高等教育治理提供了新的视角。高等教育领域具有多重法权关系,在大学的外部法权关系中,政府、市场、社会与大学四者之间存在明显的权力博弈;在大学的内部法权关系中,学校与院系、学术与行政、师生与大学管理者之间也有冲突与互动。高等教育治理的法权网络体现在两个相互渗透的层面:宏观层面和微观层面。宏观层面表现为大学的外部法权治理结构,微观层面表现为大学的内部法权治理结构。

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

ComRank： Joint Weight Technique for the Identification of Influential Communities

Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people. However, there is little work to shed light on how to rank communities while considering their levels that are determined by the quality of their published contents. In this paper, we propose solution for measuring the influence of communities and ranking them by considering joint weight composed of internal and external influence of communities. To address this issue, we design a novel algorithm called Com Rank: a modification of Page Rank, which considers the joint weight in order to identify impact of each community and ranking them. We use real-world data trace in citation network and perform extensive experiments to evaluate our proposed algorithm. The comparative results depict significant improvements by our algorithm in community ranking due to the inclusion of proposed weighting feature.

Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.

Interference Alignment in Two-Way Relay Networks via Rank Constraints Rank Minimization

Interference alignment(IA) is one of the promising measures for the multi-user network to manage interference. The rank constraints rank minimization means that interference spans the lowest dimensional subspace and the useful signal spans all available spatial dimensions. In order to improve the performance of two-way relay network, we can use rank constrained rank minimization(RCRM) to solve the IA problem. This paper proposes left reweighted nuclear norm minimization-γalgorithm and selective coupling reweighted nuclear norm minimization algorithm to implement interference alignment in two-way relay networks. The left reweighted nuclear norm minimization-γ algorithm is based on reweighted nuclear norm minimization algorithm and has a novel γ choosing rule. The selective coupling reweighted nuclear norm minimization algorithm weighting methods choose according to singular value of interference matrixes. Simulation results show that the proposed algorithms considerably improve the sum rate performance and achieve the higher average achievable multiplexing gain in two-way relay interference networks.

Implementation and Evaluation of Dynamically Weighted Low Complexity Fair Queuing(DWLC-FQ) Algorithm for Packet Scheduling in WiMAX Networks

Services provided by internet need guaranteed network performance. Efficient packet queuing and scheduling schemes play key role in achieving this. Internet engineering task force(IETF) has proposed Differentiated Services(Diff Serv) architecture for IP network which is based on classifying packets in to different service classes and scheduling them. Scheduling schemes of today's wireless broadband networks work on service differentiation. In this paper, we present a novel packet queue scheduling algorithm called dynamically weighted low complexity fair queuing(DWLC-FQ) which is an improvement over weighted fair queuing(WFQ) and worstcase fair weighted fair queuing+(WF2Q+). The proposed algorithm incorporates dynamic weight adjustment mechanism to cope with dynamics of data traffic such as burst and overload. It also reduces complexity associated with virtual time update and hence makes it suitable for high speed networks. Simulation results of proposed packet scheduling scheme demonstrate improvement in delay and drop rate performance for constant bit rate and video applications with very little or negligible impact on fairness.

A Novel Vertical Handoff Algorithm with Dynamic Weights in Wireless Heterogeneous Networks

In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.

Study on gas monitoring technology based on information fusion

In view of the deficiency of current gas monitoring systems in coal mine roadwayexcavation, a two-level information fusion technology, which adopted the adaptiveweighted algorithm and the BP neural network technology, was applied to gas monitoring.The results show that the adaptive weighted algorithm can realize self-regulation by decreasingthe weight value of the failed sensor automatically, so as to eliminate the effect ofthe failed sensor and ensure the effectiveness and accuracy of the gas monitoring system.The BP neural network can not only effectively predict the gas gush quantity of the excavationroadway, but also accurately calculate the gas concentration in the region whereone or more sensors have failed, so as to provide the basis for judging the safety status ofthe roadway excavation.The experiments prove the superiority and feasibility of the applicationof information fusion in gas monitoring.

Consistency and Asymptotic Property of a Weighted Least Squares Method for Networked Control Systems

In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

Trade-off Optimization for Scalable Video Coding Streaming in Relay-Based OFDMA Networks

To achieve an optimal trade-off between video quality and energy efficiency in the uplink streaming of multi-user Scalable Video Coding (SVC) videos in relay-based Orthogonal Frequency Division Multiple Access (OFDMA) cellular networks, a cross-layer design framework that jointly selects the Transmission Policy (TP) for SVC video frames, assigns OFDMA subcarriers, and allocates power for each subcarrier is proposed. We apply the dual decomposition method to the problem, and obtain a TP selection subproblem for each SVC video adaptation and a resource allocation subproblem of Joint Subcarrier, Relay and Power Allocation (JSRPA). A second level of dual decomposition is used to divide the JSRPA problem into independent subcarrier subproblems. The proposed Crosslayer Trade-off Optimization (CTO) algorithm is sub-distributed with significantly low complexity. A performance evaluation with typical SVC video traces demonstrates that the proposed algorithm is able to converge and efficiently achieve the optimal trade-off between the video quality and energy consumption at the MSs for uplink SVC streaming.

Copyright Protection Mode for Geological Information Sharing under the Environment for Network

Using copyright rules clear government of geological information property rights is the main path to realization of geological information socially sharing. For China＇s current laws have an institute basic principle of geological information protected by copyright law, we need to detailed thinking the copyright subject, write content, licensing and other aspects, combine the geological information sharing mechanism.

An improved multiplicative exponent weighting vertical handoff algorithm for WLAN/WCDMA heterogeneous wireless networks

An improved MEW （ muhiplicative exponent weighting） algorithm, SLE-MEW is proposed for vertical handoff decision in heterogeneous wireless networks. It introduces the SINR（ signal to interference plus noise ratio） effects, LS （least square） and information entropy method into the algorithm. An attribute matrix is constructed considering the SINR in the source network and the equivalent SINR in the target network, the required bandwidth, the traffic cost and the available bandwidth of participating access networks. Handoff decision meeting multi-attribute QoS（quality of serv- ice） requirement is made according to the traffic features. The subjective weight relation of decision elements is determined with LS method. The information entropy method is employed to derive the objective weights of the evaluation criteria, and lead to the comprehensive weight. Finally decision is made using MEW algorithm based on the attribute matrix and weight vector. Four 3GPP（ the 3rd generation partnership project） defined traffic classes are considered in performance evaluation. The simulation results have shown that the proposed algorithm can provide satisfactory performance fitting to the characteristics of the traffic.

MRT-LBM-based numerical simulation of seepage flow through fractal fracture networks

The study on seepage flow passing through single fractures is essential and critical for understanding of the law of seepage flow passing through fracture networks and the coupling mechanisms of seepage field and stress field in rock masses.By using the fractal interpolation to reconstruct a natural coarse fracture,as well as taking into account the microstructure of the fracture,the numerical simulation of seepage flow passing through the coarse fractures with two distinct vertical scaling factors is conducted based on the MRT-LBM model of the lattice Boltzmann method.Then,after obtaining the length of the preferential flow pathway,the permeability of the two kinds of fractures is estimated respectively.In view of difficulties in locating the preferential flow pathway of natural fracture networks,by numerical tests a transect permeability weighted algorithm for estimating the fracture network permeability is proposed.The algorithm is not specific to one or more particular preferential flow pathways,but considers the contribution of each section to hinder the fluid passing through the medium.In order to apply the new algorithm,by capturing the structure of fracture networks based on the image-processing technique,the numerical simulations of seepage flow passing through two groups of natural fracture networks is carried out,the permeability is forecasted and the partial flows are reproduced for both cases.It is found that the preferential flow pathway emerges at the beginning of evolution,then is strengthened subsequently,and finally reaches a steady status.Furthermore,by using the proposed method some details on local flow can be clearly observed such as backflows and vortices at local branches can exist simultaneously and so forth,suggesting the validness of the proposed method for multiscale simulations of seepage flow.