Whether the System of Punitive Damages Should Be Introduced to Better Protect the Copyright in the Digital Era

Since the system of punitive damages introducted to the "Copyright Law", domestic academics have been continuously involved in this dispute. In this paper, in the perspective of legal cases and current situation of compensation for damage of copyright infringement, analyze the introduction of punitive damages of "Copyright Law". Then, from the view of Copyright owners’ strategy of litigation and relevant judicature and legislation, analyze whether should introduce the system of punitive damages to better protect copyright, and put forward to other paths of dealing with damages, hoping to do my best to help to perfect judicature and legislation. Keywords: protection of copyright; compensation for damage of infringement; punitive damage

Necessity of Implementing Pharmaceutical Patent Linkage System in China

Pharmaceutical patent linkage system is a result of balancing the interests of the original pharmaceutical manufacturers, generic drugs manufacturers and the public, and without infringing upon the patent rights of the original patented drug, its main purpose is to make the generic drugs on sale as soon as possible. At present, domestic scholars mostly focus on the macro level of pharmaceutical patent linkage system, and rarely analyze the reasons and necessity of implementing pharmaceutical patent linkage system from the actual situation of China. On the basis of the existing research, summary the key to the successfully implementing pharmaceutical patent linkage system in the United States, and according to the experience of the developed countries and the actual conditions of China, analyze the necessity of implementing pharmaceutical patent linkage system, and give suggestions, which is very important in science and practice. Keywords: pharmaceutical patent linkage system, generic drugs, the original patented drugs, stakeholder

Cooperative Adaptive Learning Control for a Group of Nonholonomic UGVs by Output Feedback

A high-gain observer-based cooperative deterministic learning (CDL) control algorithm is proposed in this chapter for a group of identical unicycle-type unmanned ground vehicles (UGVs) to track over desired reference trajectories. For the vehicle states, the positions of the vehicles can be measured, while the velocities are estimated using the high-gain observer. For the trajectory tracking controller, the radial basis function (RBF) neural network (NN) is used to online estimate the unknown dynamics of the vehicle, and the NN weight convergence and estimation accuracy is guaranteed by CDL. The major challenge and novelty of this chapter is to track the reference trajectory using this observer-based CDL algorithm without the full knowledge of the vehicle state and vehicle model. In addition, any vehicle in the system is able to learn the knowledge of unmodeled dynamics along the union of trajectories experienced by all vehicle agents, such that the learned knowledge can be re-used to follow any reference trajectory defined in the learning phase. The learning-based tracking convergence and consensus learning results, as well as using learned knowledge for tracking experienced trajectories, are shown using the Lyapunov method. Simulation is given to show the effectiveness of this algorithm

Understanding the Evaluation Abilities of External Cluster Validity Indices to Internal Ones

Evaluating internal Cluster Validity Index (CVI) is a critical task in clustering research. Existing studies mainly employ the number of clusters (NC-based method) or external CVIs (external CVIs-based method) to evaluate internal CVIs, which are not always reasonable in all scenarios. Additionally, there is no guideline of choosing appropriate methods to evaluate internal CVIs in different cases. In this paper, we focus on the evaluation abilities of external CVIs to internal CVIs, and propose a novel approach, named external CVI\u27s evaluation Ability MEasurement approach through Ranking consistency (CAMER), to measure the evaluation abilities of external CVIs quantitatively, for assisting in selecting appropriate external CVIs to evaluate internal CVIs. Specifically, we formulate the evaluation ability measurement problem as a ranking consistency task, by measuring the consistency between the evaluation results of external CVIs to internal CVIs and the ground truth performance of internal CVIs. Then, the superiority of CAMER is validated through a real-world case. Moreover, the evaluation abilities of seven popular external CVIs to internal CVIs in six different scenarios are explored by CAMER. Finally, these explored evaluation abilities are validated on four real-world datasets, demonstrating the effectiveness of CAMER

Clustering Algorithm Based on Sparse Feature Vector without Specifying Parameter

Parameter setting is an essential factor affecting algorithm performance in data mining techniques. CABOSFV is an efficient clustering algorithm which can cluster binary data with sparse features, but it is challenging to specify the threshold parameter. To solve the difficulty of parameter decision, a clustering algorithm based on sparse feature vector without specifying parameter (CASP) is proposed in this paper. The calculation method of an upper limit of threshold is firstly defined to determine the range of threshold. Furthermore, we use the sparseness index to sort the data and conduct the clustering process based on the adjusted sparse feature vector after data sorting. An interval search strategy is adopted to find a suitable threshold within the defined threshold range, and the clustering result with the selected suitable parameter is the outcome. Experiments on 7 UCI datasets demonstrate that the clustering results of the CASP algorithm are superior to other baselines in terms of both effectiveness and efficiency. CASP not only simplifies the parameter decision process, but also obtains desirable clustering results quickly and stably, which shows the practicability of the algorithm

The Research on Coordinated Decision-Making Method Tax System Based on Subject Data

Academically, the research of subject database of tax system aims to set up an efficient, harmonious virtual data application environment. Subject data, in application and management, has been on demand polymerized and autonomously collaborated and has reached a balance between instantaneity and accuracy. This paper defines the connotation and characteristics enterprise informationization, designs a value system of enterprise informationization which is subject database oriented, and builds a model for the import of the subject database of enterprise informationization. Meantime, this paper describes the structure of the subject database based information import model and forges the model’s theoretical basis of subject data import in tax system. Using the model can make an analysis on the information of data warehouse, storage information, and tax information to provide decision support for the tax administrators