In machine translation (MT) practice, there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions. The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the ‘ZHE’ aspect (ZHE Rule). A ZHE classification model was built in this study. The impacts of each set of temporal, lexical aspectual, and syntactic features, and their integrated impacts, on the accuracy of the ZHE Rule were tested. Over 600 misclassified corpus sentences were manually examined. A 10-fold cross-validation was used with a decision tree algorithm. The main results are: (1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics: the precision rate and the areas under the receiver operating characteristic curve (AUC). (2) The temporal, lexical aspectual, and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule. The syntactic and temporal features have an impact on ZHE aspect derivations, while the lexical aspectual features are not predictive of ZHE aspect derivation. (3) While associated with active verbs, the ZHE aspect can denote a perfective situation. This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice. The machine learning method, decision tree, can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Density-based nonparametric clustering techniques, such as the mean shift algorithm, are well known for their ?exibility and effectiveness in real-world vision-based problems. The underlying kernel density estimation process can be very expensive on large datasets. In this paper, the divide-and-conquer method is proposed to reduce these computational requirements. The dataset is first partitioned into a number of small, compact clusters. Components of the kernel estimator in each local cluster are then ?t to a single, representative density function. The key novelty presented here is the ef?cient derivation of the representative density function using concepts from function approximation, such that the expensive kernel density estimator can be easily summarized by a highly compact model with very few basis functions. The proposed method has a time complexity that is only linear in the sample size and data dimensionality. Moreover, the bandwidth of the resultant density model is adaptive to local data distribution. Experiments on color image ?ltering/segmentation show that, the proposed method is dramatically faster than both the standard mean shift and fast mean shift implementations based on kd-trees while producing competitive image segmentation results.

Traditional gradient domain seamless image cloning is a time consuming task, requiring the solving of Poisson’s equations whenever the shape or position of the cloned region changes. Recently, a more efficient alternative, the mean-value coordinates (MVCs) based approach, was proposed to interpolate interior pixels by a weighted combination of values along the boundary. However, this approach cannot faithfully preserve the gradient in the cloning region. In this paper, we introduce harmonic cloning, which uses harmonic coordinates (HCs) instead of MVCs in image cloning. Benefiting from the non-negativity and interior locality of HCs, our interpolation generates a more accurate harmonic field across the cloned region, to preserve the results with as high a quality as with Poisson cloning. Furthermore, with optimizations and implementation on a graphic processing unit (GPU), we demonstrate that, compared with the method using MVCs, our harmonic cloning gains better quality while retaining real-time performance.

Collaborative access control is receiving growing attention in both military and commercial areas due to an urgent need to protect confidential resources and sensitive tasks. Collaborative access control means that multiple subjects should participate to make access control decisions to prevent fraud or the abuse of rights. Existing approaches to access control cannot satisfy the requirements of collaborative access control. To address this concern, we propose an authorization model for collaborative access control. The central notions of the model are collaborative permission, collaboration constraint, and collaborative authorization policy, which make it possible to define the collaboration among multiple subjects involved in gaining a permission. The implementation architecture of the model is also provided. Furthermore, we present effective conflict detection and resolution methods for maintaining the consistency of collaborative authorization policies.

We improve inverse reinforcement learning (IRL) by applying dimension reduction methods to automatically extract abstract features from human-demonstrated policies, to deal with the cases where features are either unknown or numerous. The importance rating of each abstract feature is incorporated into the reward function. Simulation is performed on a task of driving in a five-lane highway, where the controlled car has the largest fixed speed among all the cars. Performance is almost 10.6% better on average with than without importance ratings.

We investigate the use of two integer inversion algorithms, a modified Montgomery modulo inverse and a Fermat’s Little Theorem based inversion, in a prime-field affine-coordinate elliptic-curve crypto-processor. To perform this, we present a low-power/energy GF(p) affine-coordinate elliptic-curve cryptography (ECC) processor design with a simplified architecture and complete flexibility in terms of the field and curve parameters. The design can use either of the inversion algorithms. Based on the implementations of this design for 168-, 192-, and 224-bit prime fields using a standard 0.13 μm CMOS technology, we compare the efficiency of the algorithms in terms of power/energy consumption, area, and calculation time. The results show that while the Fermat’s theorem approach is not appropriate for the affine-coordinate ECC processors due to its long computation time, the Montgomery modulo inverse algorithm is a good candidate for low-energy implementations. The results also show that the 168-bit ECC processor based on the Montgomery modulo inverse completes one scalar multiplication in only 0.4 s at a 1 MHz clock frequency consuming only 12.92 μJ, which is lower than the reported values for similar designs.

We extract a mathematical model to simulate the steady-state charging and discharging behaviors of an electrochemical storage over a 24-hour time interval. Moreover, we develop a model for optimizing the daily operational planning of an interconnected micro grid considering electrochemical storage. The optimization model is formulated to maximize the total benefit of the micro grid via selling power to its end consumers and also exchanging power with the wholesale energy market so that the constraints of distributed energy resources (DERs) and low-voltage grid are met. The optimization problem is solved by a genetic algorithm, and applied on two micro grids operating under different scenarios containing the absence or presence of electrochemical storages. Comparison of the results of the optimization model for this micro grid, with and without electrochemical storage, shows that the electrochemical storage can improve the economical efficiency of the interconnected micro grids by up to 10.16%.