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Link prediction (LP) is an attractive research problem on social network data. Yet, the link information may be leaked by the untrusted collector. As countermeasures, there are a few methods specially designed for link prediction under local differential privacy (LDP), which allow the third-party data collector collects user data for link prediction while protecting user connection privacy. In this paper, we propose a Community-based graph collection with Personalized sampling Randomized Response (CPRR) which is a novel graph collection algorithm with LDP and reaches decent trade-off between sensitive link protection and link prediction performance. The proposed mechanism adopts a personalized sampling technique for each connection of a user and then utlizes the randomized response mechanism on the sampled subset. Based on the personalized sampling technique, we can reduce the injected noise in LDP. Meanwhile, considering the different edge distributions of the different regions on the original graph, we propose a community-based sampling strategy. Then, we prove that the proposed CPRR satisfies LDP. %We compare the performance of CPRR and other privacy-preserving graph collection methods under LDP in terms of link prediction accuracy. Through extensive experiments on several real-life graph datasets, we demonstrate that CPRR can achieve better results in balancing privacy protection and link prediction performance than the state-of-art baselines

The inefficient charge separation and transport (CST) process has been a major obstacle that limits the catalytic activity of polymeric photocatalysts. Here we propose a symmetry-breaking strategy to regulate the internal electric field (IEF) in crystalline polymers for enhanced CST and photocatalytic efficiency. As a proof of concept, two crystalline linear copolymers, p-phenylenediamine/m-phthalaldehyde (PDA-MAH) and p-phenylenediamine/p-phthalaldehyde (PDA-PAH), were synthesized. By breaking the lattice symmetry, we observed robust IEF in the non-centrosymmetric PDA-MAH, as compared to the centrosymmetric PDA-PAH. Such strong IEF was extended throughout the catalyst bulk to accelerate the CST process in PDA-MAH, resulting in a photocatalytic disinfection performance under visible light irradiation that surpassed the best of the previously reported polymeric photocatalysts. This work presents a promising paradigm for tailoring polymeric semiconductors with efficient CST process.

Nonlinear factors exist in electro-hydraulic shaking table and may distort the response signal when a sinusoidal acceleration is inputted. To obtain accurate harmonics, a harmonic identification strategy for the acceleration of the electro-hydraulic shaking table based on the SA-PSO (Simulated Annealing-Particle Swarm Optimization) algorithm was proposed. First, the criterion function of harmonic identification was built with the least square method. Then, the PSO algorithm was introduced into the internal circulations of the SA algorithm to speed up the local search speed to construct the harmonic identification strategy. To verify the performance of the identification strategy, the SA algorithm, PSO algorithm, and SA-PSO algorithm were successively used to identify the response signal. And the convergence rate, accuracy, and stability of the three algorithms were compared. Finally, the harmonic regeneration was carried out after the amplitude and phase of each harmonic were obtained. Experimental results show that the harmonic identification strategy has fast identification speed and high identification accuracy.

It is important to monitor the iced condition for operating wind turbine blades. However, most sensors are difficult to be used in this quite cold and lightweight environment. Although the fiber bragg grating (FBG) is a good candidate for the case. With the change of the temperature, the thermal expansion coefficient and thermo-optical coeffecient of FBG material will affect the accuracy of characteristics of FBG sensor. Aiming at the application of fiber optic sensor in low temperature environment and ultrathin requirement, the low temperature characteristics of FBG sensor with free arc shape package has been studied. The low temperature calibration experiments of two packaged FBG samples are tested from -35°C ~20°C in a constant temperature freezer with alcohol tank. The effects of low temperature characteristics of two FBGs packaged with free straight and arc shape tubes are compared. The results show that arc shape can effectively reduce the repeatability error and improve its accuracy due to providing possible low temperature contracting of the optical fibers. Flat arc shape makes it possible to package ultrathin FBG temperature sensor.

Uniform AgCl nanocubes with an average size of about 360 nm have been synthesized. Under UV illumination, a layer of silver nanoparticles with surface plasmon resonance was produced and covered the surface of AgCl particles in a short time, and the Ag/AgCl particles remained quite stable as shown in XRD patterns. The as-synthesized AgCl particles were tried for the first time as photoinitiator to polymerize N-isopropylacrylamide under UV-irradiation, and a high efficiency and conversion rate were realized. The structure of the polymers was studied by FTIR spectrophotometer. The results of turbidimetric method showed that the polymers were temperature sensitive and their lower critical solution temperature was about 33 °C. The effects of photoinitiators concentration, monomers concentration, and irradiation time on the polymer yield were investigated. The reaction mechanism was discussed by using salicylic acid as scavengers of hydroxyl radicals and conducting the reaction under oxygen atmosphere, respectively. The results proved that hydroxyl radicals were essential to chain initiation.

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes., Comment: accepted by ACL 2018

A pyrene derivative as the donor and a butyl-viologen as the acceptor were used to construct a novel charge-transfer cocrystal consisting of mixed-stacking structure and having switchable photoconductivity stemming from the donor-acceptor heterojunctions within the lattice.

In this paper, we investigate object tracking in video sequences and propose a particle filter based tracking algorithm with color and texture information fusion, in which the target model is jointly represented by spatial-weighted color histogram and LBP (Local Binary Patterns) texture histogram. The property of local grayscale or color invariance for LBP operator makes it more reliable to measure the spatial structure of local image texture. The system is less sensitive to illumination changes and partial occlusions, and can be able to track objects in diverse conditions. Experimental results demonstrate that the performance of the proposed method is more robust and accurate than the original color based method, especially when tracking objects with similar color appearance to the background and partial occlusions.

This paper presents a modified codebook model for real-time moving foreground detection. The proposed method is an effective combination of background modeling and motion detection. Without a long training sequence, the background model can be represented in a compressed form, a series of codebooks, which means sample background values for each pixel are quantized into codebooks that can used in detection process. In this way, we can capture the structural variation of background in different conditions such as periodic-like motion , hostile environment or change of scene caused by moving object over a long period of time under limited memory. Compared with the original codebook model, this proposed method is more efficient in computation and takes up less memory. Experimental results show that the proposed algorithm is effective, quick for motion detection, and can meet the demands of real-time applications.

This paper proposes a novel colorization algorithm for monochrome still images based on fuzzy clustering and distance transformation. Given small amount of typical color scribbles manually marked on the input grayscale image, the followed colorization process consists of four main steps: color scribble extraction, distance transformation and spatial weight estimation, fuzzy clustering and luminance weight estimation, and weighted color blending. By propagating the local color hints from the given scribbles to the whole grayscale image, the final output color image can be produced. Experimental results show that when the content of grayscale image is relatively simple, the proposed colorization method can efficiently gain a good colorized image with only a small number of scribbles. The algorithm is simple and easy to implement. Moreover, it takes only several seconds to complete the colorization process but can get visually vivid result.

This paper presents a simple exemplar-based method for 2D image texture synthesis. The contribution of the proposed algorithm lies in the effective combination of MEBC (Minimum Error Boundary Cut) and LBP (Local Binary Patterns) feature. Given a texture sample, the corresponding LBP image is firstly generated, then block matching is carried out based on LBP image and the candidate block with MEBC boundary is pasted on the output image. Experimental results for a wide range of texture samples demonstrate that the performance of this LBP-based method is equal to or better than that of the original image quilting algorithm, especially for semi-structured and stochastic textures. Furthermore, the proposed method can obtain a particularly effective synthesized result even for the texture samples that image quilting fails. For regular texture synthesis, the calculation of MEBC can be further omitted and therefore the resulted simplified version of the proposed method can even achieve a good performance in a shorter time.