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As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CAr_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CAr_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.

Recently, deep learning based compressive sensing (CS) methods show superior reconstruction performance. However, these approaches are restricted for compressive video sampling in practical design of imaging devices. In this paper, we propose a novel deep network-based CS framework for efficient optical implementation. The proposed framework, dubbed DSCR-Net, consists of a diffractive sensing network employing light diffraction for efficient sampling and a complex-valued neural network for reconstruction, respectively. The diffractive sensing network can achieve real-time sampling at the speed of light. Furthermore, complex-valued neural network is developed to facilitate the reconstruction quality from the complex-valued measurements by jointly considering their real and imaginary parts. Extensive experiments demonstrate that DSCR-Net outperforms the state-of-the-art CS methods in the low sampling-rate region with a potential of immediate implementation of imaging device.

Reversible residual network naturally extends the linear lifting scheme with no theoretic guarantee. In this paper, we propose a reversible autoencoder (Rev-AE) with this extended non-linear lifting scheme to improve image reconstruction. Nonlinear prediction and update operators are designed based on shallow convolutional neural networks to model multilayer non-linearities. Different from existing autoencoders, Rev-AE support efficient image reconstruction with parameters reusable for the symmetric encoder and decoder. Rev-AE forms a set of related frames to guarantee perfect reconstruction with the non-linear extension of classic lifting scheme. Lower and upper bounds are developed for the set of frames to relate with the singular values for each non-linear operator. Furthermore, we employ Rev-AE into lossy image compression to evaluate its effectiveness on image reconstruction. Experimental results show that Rev-AE achieves competitive performance in comparison to the state-of-the-art.

Generative model has emerged as a disruptive alternative for lossy compression of natural images, but suffers from the low-fidelity reconstruction. In this paper, we propose a noise-to-compression variational antoencoder (NC-VAE) to achieve efficient rate-distortion optimization (RDO) for end-to-end optimized image compression with a guarantee of fidelity. The proposed NC-VAE improves rate-distortion performance by adaptively adjusting the distribution of latent variables with trainable noise perturbation. Consequently, high-efficiency RDO is developed based on the distribution of latent variables for simplified decoder. Furthermore, robust end-to-end learning is developed over the corrupted inputs to suppress the deformation and color drift in standard VAE based generative models. Experimental results show that NC-VAE outperforms the state-of-the-art lossy image coders and recent end-to-end optimized compression methods in low bit-rate region, i.e., below 0.2 bits per pixel (bpp).

Filter bank based convolutional networks (FBCNs) enable efficient separable multiscale and multidirectional decomposition with a convolutional cascade of 1-D radial and directional filter banks. In this paper, we propose a two-stage subband coding framework for FBCN analysis coefficients using a SPIHT-like algorithm and subsequent primitive-based adaptive arithmetic coding (AAC). The SPIHT-like algorithm extends spatial orientation tree to exploit inter-subband dependency between subbands of different scales and directions. Mutual information is estimated for information-theoretical measurement to formulate such dependencies. Various primitives are designed adaptively encode the generated bitstream by fitting its varying lists and passes. Neural networks are leveraged to improve probability estimation for AAC, where nonlinear prediction is made based on contexts regarding scales, directions, locations and significance of analysis coefficients. Experimental results show that the proposed framework improves the lossy coding performance for FBCN analysis coefficients in comparison to the state-of-the-arts subband coding schemes SPIHT.

Sparql (SPARQL Protocol and RDF Query Language) is a query language and data acquisition protocol designed for RDF development. Although it is defined for the RDF data model developed by the W3C, it can be used in any form of RDF to represent data resources. With the explosive growth of web information resources, more and more data is using RDF structure. The research and obtaining of useful information in massive data has become a major challenge. Efficient search and effective query has become the focus attention of research. In this paper, we design an efficient optimization method by finding a semantic connection chain in the system (SparkIlink). Data was stored on the file system of hadoop (HDFS). Based on Spark framework with efficient distributed memory, this system has achieved efficient searching and optimizing performance for massive RDF data. Our work includes the following mechanism: (1) using vertical partition as data storage structure; (2) using twice data statistics; (3) using information connection chain based on semantic. Our system can support massive triples query in distributed environment to achieve efficient query processing. The experiment of this paper is based on the latest SPARQLGX on the spark platform RDF system. In contrast, our system is more efficient in data search than SPARQLGX.

In order to realize the Power Demand Response of residents, this paper designs the smart-home appliance demand management system based on intelligent gateway, and using Apriori algorithm to tap the user's association rules, and through the data preprocessing according to the peak-flat-valley period of the demand response, so that the algorithm can process the time information in the data, and finally realized the association rules mining for the residents' usage habits. Finally, a demand response control scheme based on association rules is proposed, which provides a theoretical basis for the real-time response of the user side.

Neighbors embedding is a promising method for single image super-resolution (SR). However, the fixed number of neighbors for different kind of input low resolution (LR) patches may be improper. In addition, the assumption that low resolution space and high resolution (HR) space has similar local geometry leads to improper HR patches are used for reconstruction. In this paper, we propose a novel single image super-resolution method based on low-rank representation and metric learning. Low-rank representation aims to exclude outliers in neighbors, and metric learning aims to learn a linear projection matrix so that LR space with the transformed metric and HR space have similar local structure. Experiments on fourteen images show that our method obtains the best results on most images compared with traditional methods, which illustrates the effectiveness and superiority of the proposed methods.

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

The national cultural information resources sharing project is a key project in China's public cultural service, it is a new carrier of the construction of public cultural service system, it has important significance to narrow the gap between urban and rural areas, gradually eliminate the digital divide, satisfy the people's cultural needs, improve the construction of public cultural service system. This paper is based on the shared value engineering analysis of the culture, point at the a prominent development bottleneck of the construction of the Cultural Sharing Project, pull-in the cloud computing technology and management ideas, using cloud computing to help reduce operation and maintenance costs of Cultural Sharing Project. It can improve work efficiency, service innovation, and enhance the cultural service ability.

Present a novel method for 3D shape measurement of specular surface based on structured light coding method. Coded fringe pattern are projected onto the high reflected surface of the measured object. The deflection fringe pattern that modulated by the object surface are captured by the CCD camera. Central perturbation phase-shift technology is adopted to fast retrieve the phase distribution on the deflection fringe pattern. Then the slope information can be obtained by analyzing the relationship between the phase deflection and the slope of the object surface. By combining the character of the phase-shifting fringe pattern and digital image processing technologies the boundary of the measured object can be picked up from the background before reconstruction, which can reduce the quantities of data to be processed and advance the reconstruction precision at the boundary. The improved wave-front reconstruction method is used to reconstruct the surface. With the application of this technology the 3D shape of specular surface can be precisely reconstructed.

Based on a novel method that combining the phase deflectormetry technology and the white light scanning interferometry technology to realize precision measurement of specular surfaces, the design of the white light scanning interference measurement system is completed. On the basis of the thorough research of white light scanning interference measuring principle two mainly used white light peak detection algorithms are studied. Simulation experiment proving the validity of the algorithms is conducted and high measurement precision is reached. Influences of the system parameters (spectrum width and step interval), noise and the error of micro displacement on the measurement precision are analyzed. Reconstruction of 3D simulation steps varying from 10μm −0.05μm–40μm is completed.

The evaluation of public service performance plays a significant part in government's performance management system. It is the mechanism of satisfying the public demands and constructing the service-oriented society. However, at present there are still some of the traditional, backward management philosophy and value orientation existing, which are restricting the implementation of public service performance evaluation, and to a certain extent, hindering the improvement of the functions of the government. This article focuses on the relationship between performance evaluation and performance culture and analyses its impact upon performance evaluation in public sector in terms of performance culture. Based on rigorous and critical analysis, the author draws a conclusion that there is a significant correlation between performance culture and performance evaluation and performance culture has a positive impact on the evaluation of public service performance.

The technology of asphalt pavement has been changed greatly and many methods have been developed to improve the performance of asphalt pavement. Among them, the SBS or TLA reinforced asphalt pavement is a successful alternative both domestically and overseas. More attentions have been paid to it and it is expectative because of both its eminent performance and simple construction technology, and its economical preponderance. Based on the use experience of present airport runway, this paper studies the compound modification of SMA in airport runway using SBS and TLA. The paper systematically elaborated on the basic concept, characteristic and the development of usage of SMA, as a new kind of pavement material using in the runway. At first, select airport common gradations, use SBS and TLA compound modified asphalt, and contrast respective performance. Then the test method of the design is narrated. The test results show that SMA-16 using SBS and TLA have a noticeable improvement in the aspects of high temperature performance and water stability. After studying on, the detailed experiments are carried on according to the key problems in SMA design, some improvement measures are also devised from the comparison of results. Further proposal is suggested for the later projects.

With more and more applications of workflow technology, the workflow systems must be flexible and dynamic in order to effectively adapt for the uncertain and error-prone collaborative work environments. This paper adds the interaction and machine learning to the workflow model proposed by Workflow Management Coalition and then applies the parallel particle swarm optimization algorithm to solve it, so that workflow modeling and enactment are both flexible while the complexity of whole system is decrease. The improvement based on the model manifest that it not only realizes the flexible workflow, but also supports the personality of workflow.

Graduate synthesis evaluation marks have become a reference for the graduate educational administrative organizations to assess the scholarship and outstanding graduate, and an important basis for the employing unit to understand the graduate school performance. Traditional evaluation method usually adopts quantitative conventional mathematics hard partition methods, which exists large disadvantages. The cloud theory can synthesizes the fuzziness and the randomness of the qualitative concept together to realize the conversion between quantitative value and qualitative value. It is the important tool to study the uncertainty. The cloud theory is applied to the qualitative evaluation of student comprehensive quality, which can effectively make the students soft partition and obtain the higher scientific evaluation result. Based on the cloud theory, this paper put forward a new model of graduates' comprehensive quality. The practice proved that this model can objectively and accurately transform the political and thought quality, practical ability, physical and mental quality and learning ability into qualitative evaluation. It can show the comprehensive quality of graduates better.

The demand for cheap broadband Internet for nomadic users has created a market for Internet sharing. Wi- Fi communities which allow their users to share their wired Internet connections have emerged and become increasingly popular. Organizations like FON promise to provide free wireless Internet access in many places. However, user authentication is the Achilles heel of these systems. A user that allows other community members to use its access point must expect to be held responsible for other users' actions. Moreover, these Wi-Fi sharing systems are often insecure which allows eavesdroppers to gather sensitive information on the wireless link. This work provides efficient, scalable, and secure access control for large Wi-Fi sharing systems. The host identity protocol (HIP) is used as a building block for a solution which supports strong user authentication as well as mobility support for nomadic users. In our presentation, we show the feasibility and effectiveness of this approach by demonstrating the PISA authentication protocol in action.

To enlarge the measuring range of inductive displacement sensor, the proposed displacement measurement instrument consists of four elements: the first is a movable coil with short length supplied with DC voltage. The second is a fixed iron rod acting as a core, its shape is designed carefully and the cross-section is varying along the axis. The third is an iron cover. It works as the external path of magnetic flux. When the coil moves on the rod, the current induced in the coil is proportional to the axis-direction displacement of the iron rod. The last one is a measurement circuit used to transform the current signal to the voltage signal and then processed by a microprocessor. This sensor offers excellent features of smart structure, low cost, large sensing length (1400mm), high accuracy (0.5%) and good linearity. An application of this instrument has been put on use in the underwater displacement measurement of a water station lift-gate control system.