Wireless radio frequency (RF) protocol of the digital home runs on control equipments, RF repeater and terminal equipments. RF protocol provides a quick, flexible, and reliable means to communicate, identify, track, a...
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Previous work on the one-class collaborative filtering （OCCF） problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is t...
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Previous work on the one-class collaborative filtering （OCCF） problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression （CTR）, usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation （LDA） model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user＇s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation *** further combine this method with a matrix factorizationbased method to tackle the multi-class collaborative filtering （MCCF） problem, which also achieves better performance on predicting user ratings.
A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by ***,when the nu...
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A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by ***,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes *** this paper,we propose a symbolic approach to reasoning about quantum *** is based on a small set of laws involving some basic manipulations on vectors and *** symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.
Agricultural resource data management system has continuously promoted utilization of agricultural resource and development of rural economics. The platform mentioned in this paper is the one of agricultural resource ...
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Agricultural resource data management system has continuously promoted utilization of agricultural resource and development of rural economics. The platform mentioned in this paper is the one of agricultural resource data and service based on SOA, which is combined with 3S technology, Web service technology, spatial database technology, components technology and .Net technology. Systems on different administrative levels or in different fields could be connected to the platform through the platform's components and service interfaces. The platform structure, function, database design and the innovation in the process of platform design and development were introduced.
A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimati...
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A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom （WLS-AIZ） For a given zooming ratio n, every pixel in a low-resolution （LR） image is associated with an n x n block of high-resolution （HR） pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio （PSNR）, structural similarity index （SSIM） and feature similarity index （FSIM）, WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.
The growing complexity of distributed systems in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand ...
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Recommender systems play an important role in e-commerce. This paper discusses three classical methods-offline analytics, user study, and online experiment-to evaluate the performance of recommender systems and also a...
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The quantitative analyses of Nuclear Power Plant (NPP)'s repairable systems are conventionally Markov-based methods. The thing is, systems' state space grows exponentially with the increase of basic events, wh...
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The quantitative analyses of Nuclear Power Plant (NPP)'s repairable systems are conventionally Markov-based methods. The thing is, systems' state space grows exponentially with the increase of basic events, which makes the problem hard or even impossible to solve. In addition, the maintenance/test activities are frequently imposed on some safety-critical components, which make the Markov based approach unavailable. In this paper, a new numerical simulation approach based on MCSS (Minimal Cut Sequence Set) is proposed, which can get over the shortcomings of the conventional Markov method. Two typical cases are analyzed and results indicate that the new approach is correct as well as feasible.
The quantitative calculations of Nuclear Power Plant (NPP)'s repairable system are mainly based on Markov model. However, with the increase of the system's size, the system's state space increases exponent...
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The quantitative calculations of Nuclear Power Plant (NPP)'s repairable system are mainly based on Markov model. However, with the increase of the system's size, the system's state space increases exponentially, which makes the problem hard or even not to be solved. This paper proposes a method about quick calculation of Dynamic Fault Tree (DFT) for NPP's repairable system based on Minimal Cut Sequence Set (MCSS), which divides a complex DFT into individual failure chain defined by MCSS. For each failure chain, the Markov model is applied. Then the unavailability of system is obtained synthesizing the result of each failure chain. This approach decreases the system's size increasing from exponentially to linearly and reduces the computation complexity. As to the NPP's dynamic systems with low failure rate and high repair rate, this approach can give a solution with a high-precision and conservative result and has practical value.
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