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The primary key in novel recommendation is to judge whether an item is "new" for target users. The novelty of recommended items to users changes with time. Therefore, the effects of time factors on user preference, as well as the novelty and accuracy of the recommended items, have become key to novelty recommendation research. To increase novelty based on recommendation accuracy, a novel recommendation algorithm based on time-aware was proposed. First, a time attenuation parameter was introduced into a traditional algorithm to strengthen the weights of recently adopted items. Next, the calculation of innovation adoption activism was adjusted through time information. Differences among recommended items were calculated by combining the time of items that have been adopted by users and the quantity of relevant categories. Lastly, the novelty of recommended items was calculated by the result fusion strategy for Top-N recommendation. Meanwhile, the effectiveness of the proposed algorithm was verified by conducting an offline experiment. Results demonstrate that the novelty of the recommendation list by the proposed algorithm is significantly higher than that of traditional algorithms. Novelty when the number of items in the candidate set reaches 300-400 reaches the peak. The recall rate of unknown items increases by 25%, whereas the recall rate of known items decreases by 60%. The average popularity of the recommendation list decreases by 50%, and the coverage rate increases by 200%. The proposed algorithm improves the ability of the recommendation system (RS) to explore novel items. This study provides references for improving the satisfaction of users with RSs. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology and Internet computing, recommendation systems of various forms have been used in nearly all large-scale e-commerce platforms to different degrees. Besides accuracy, novelty also influences the satisfaction of users with these recommendation systems. To be considered novel, an item should show some differences from the preferences of users. To recommend diverse items under the precondition of guaranteeing accuracy, a clustering-based novel recommendation algorithm was proposed in this study. User preference was modeled by using global clustering and related-items clustering (RC), weighted distance was employed to calculate the dissimilarity of the to-be-recommended items from user preference, and item novelty was measured by combining satisfactory and unknown, followed by recommendation. The effectiveness of the proposed algorithm was verified by conducting an offline experiment. Results show that the recall rate of unknown items of RC increases by 15% whereas that of known items decreases by 60%, thereby indicating that the novelty of the recommendation result is remarkably improved. Meanwhile, the coverage rate increases by approximately 200%, thereby indicating that the proposed recommendation system demonstrates an improved ability to recommend long-tail items. This study provides references for improving the satisfaction of users with recommendation systems. [ABSTRACT FROM AUTHOR]

Determining whether an item is "novel" for target users is a crucial problem and understanding their preference and awareness degree of the item has been widely investigated in novelty recommendations. A recommendation algorithm based on "diffusion of innovation" theory was proposed in this study to verify the novelty of recommendation results on the precondition of ensuring their accuracy. Items were clustered using the K-means clustering algorithm. Users' positivity of innovation adoption for each item was calculated on the basis of the items adopted by the users, and then the novelty of the items for target users was calculated by combining their popularity change, user preference, and user difference. Results of traditional recommendation algorithms were integrated for recommendation on the basis of fusion strategy results. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that the novelty of the recommendation list of the proposed algorithm is remarkably higher than that of traditional algorithms. The novelty is high when the quantity of alternative sets reaches 400 or more, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 100%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems. [ABSTRACT FROM AUTHOR]

This paper develops a novel algorithm, termed SPARse Truncated Amplitude flow (SPARTA), to reconstruct a sparse signal from a small number of magnitude-only measurements. It deals with what is also known as sparse phase retrieval (PR), which is NP-hard in general and emerges in many science and engineering applications. Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR solver. On the theoretical side, for any n-dimensional k-sparse (k ≪ n) signal x with minimum (in modulus) nonzero entries on the order of (1/√k)||x||2, SPARTA recovers the signal exactly (up to a global unimodular constant) from about k2 log n random Gaussian measurements with high probability. Furthermore, SPARTA incurs computational complexity on the order of k2 n log n with total runtime proportional to the time required to read the data, which improves upon the state of the art by at least a factor of k. Finally, SPARTA is robust against additive noise of bounded support. Extensive numerical tests corroborate markedly improved recovery performance and speedups of SPARTA relative to existing alternatives. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is driving innovation in an ever-growing set of application domains such as intelligent processing for autonomous robots. For an autonomous robot, one grand challenge is how to sense its surrounding environment effectively. The Simultaneous Localization and Mapping with RGB-D Kinect camera sensor on robot, called RGB-D SLAM, has been developed for this purpose but some technical challenges must be addressed. Firstly, the efficiency of the algorithm cannot satisfy real-time requirements; secondly, the accuracy of the algorithm is unacceptable. In order to address these challenges, this paper proposes a set of novel improvement methods as follows. Firstly, the ORiented Brief (ORB) method is used in feature detection and descriptor extraction. Secondly, a bidirectional Fast Library for Approximate Nearest Neighbors (FLANN) k-Nearest Neighbor (KNN) algorithm is applied to feature match. Then, the improved RANdom SAmple Consensus (RANSAC) estimation method is adopted in the motion transformation. In the meantime, high precision General Iterative Closest Points (GICP) is utilized to register a point cloud in the motion transformation optimization. To improve the accuracy of SLAM, the reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework. The effectiveness of the improved algorithm has been verified by testing on standard data and comparing with the ground truth obtained on Freiburg University's datasets. The Dr Robot X80 equipped with a Kinect camera is also applied in a building corridor to verify the correctness of the improved RGB-D SLAM algorithm. With the above experiments, it can be seen that the proposed algorithm achieves higher processing speed and better accuracy. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to propose a novel on-line print-defect detecting approach. Design/methodology/approach - The proposed method uses incremental principal component analysis (IPCA) to model a variety pattern with respect to the detected image itself. Findings - The algorithm is constructed and deployed to a real-time detecting print-defect system, and the test results show that the system reduces false alarms dramatically. Originality/value - The paper describes groundbreaking work which, for the first time in the printing industry, uses IPCA in relation to print-defect detecting. [ABSTRACT FROM AUTHOR]

A novel data structure is proposed for magnitude-ordering 3-D wavelet transform coefficients. This data structure consists of temporal 1-D orientation trees followed by spatial 2-D orientation trees. Based on this data structure, a coding algorithm is developed for the embedded coding of 3-D wavelet transform coefficients. Experiment results confirm that the proposed coding algorithm outperforms a conventional algorithm that uses asymmetric 3-D orientation trees. [ABSTRACT FROM AUTHOR]

This paper presents a framework for high-level design validation using an efficient register-transfer level (RTL) automatic test pattern generator (ATPG). The RTL ATPG algorithm first generates a test environment for each validation objective, which includes variable assignments, conditional statements, and arithmetic expressions in the hardware description language (HDL) description. The test environment for a given validation objective is a set of symbolic conditions that allow for full controllability and observability of that objective. After the RTL ATPG terminates, a back-end translator intelligently translates the test environments into validation vectors by filling in the necessary values. Since the observability of error effect is naturally handled by the RTL ATPG algorithm, this approach is superior to most existing validation methods, which only focus on the excitation of HDL constructs. A set of heuristics is proposed to utilize high-level circuit information to enhance the RTL ATPG algorithm and to maximize the validation efficiency. The RTL ATPG algorithm is also coupled with an improved RTL validation-coverage metric, which can help users to gain a higher degree of confidence on the quality of generated validation vectors. The usage of the coverage metric also results in the generation of compact vector sets. Experimental results on academic and industrial benchmark circuits demonstrate that our method is able to obtain very high design-error coverage in short execution times. [ABSTRACT FROM AUTHOR]

In this paper a new predictor-corrector noninterior method for LCP is presented, in which the predictor step is generated by the Levenberg-Marquadt method, which is new in the predictor-corrector type methods, and the corrector step is generated as in [3]. The method has the following merits: (i) any cluster point of the iteration sequence is a solution of the P0 LCP; (ii) if the generalized Jacobian is nonsingular at a solution point, then the whole sequence converges to the (unique) solution of the P0 LCP superlinearly; (iii) for the P0 LCP, if an accumulation point of the iteration sequence satisfies the strict complementary condition, then the whole sequence converges to this accumulation point superlinearly. Preliminary numerical experiments are reported to show the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with disparity estimation and the reconstruction of intermediate views from stereoscopic images. Using block-wise maximum-likelihood (ML) disparity estimation, it was found that the Laplacian model outperformed the Cauchy and Gaussian models in terms of disparity compensation errors and the number of correspondence matches. The disparity values in occluded regions were then determined using both object-based and reliability-based interpolation. Finally, an adaptive technique was used to interpolate the intermediate views. One distinguishing characteristic of this algorithm is that the left and right-eye images were projected onto the plane of the intermediate view to be reconstructed. This resulted in two projected images. The intermediate view was created using a weighted average of these two projected images with the weights based on the quality of the corresponding areas of the projected images. Subjective examination of the reconstructed images indicate that they have high image quality and good stable depth when viewed stereoscopically. An objective evaluation with the test image sequence "Flower Garden" shows that the proposed algorithm can achieve a peak signal-to-noise ratio gain of around 1 dB, when compared to a reference algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we convert the linear complementarity problem to a system of semismooth nonlinear equations by using smoothing technique. Then we use Levenberg-Marquardt type method to solve this system. Taking advantage of the new results obtained by Dan, Yamashita and Fukushima [11, 33], the global and local superlinear convergence properties of the method are obtained under very mild conditions. Especially, the algorithm is locally superlinearly convergent under the assumption of either strict complementarity or certain nonsingularity. Preliminary numerical experiments are reported to show the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x, y) = 0 by using the famous NCP function-Fischer-Burmeister function. Note that some equations in H(x,y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H (x,y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K-T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point. However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is Po matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method. [ABSTRACT FROM AUTHOR]

Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement. However, the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces, which gives the deconvolved result strong ambiguity and poor spatial continuity. To alleviate this issue, we developed a structurally constrained deconvolution algorithm. The proposed method extracts the reflection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial reflection regularization. Benefiting from the introduction of the reflection regularization, the proposed method enhances the stability and spatial continuity of conventional deconvolution methods. Synthetic and field data examples confirm the correctness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Dynamic optimization is likely to play an important role in the reliability, quality, and efficiency of future power system, specifically for microgrid with renewable generation. This paper presents an improved hybrid optimization algorithm, called information exchange particle swarm optimization (IEPSO), to deal with the problem of dynamic optimal control in a microgrid with multiple distributed generations (DGs), such as wind power, small hydro, and energy storage. Based on the actual application of DG in Southwest China, we propose an optimization model for grid-connected microgrid that introduces a contribution rate of energy storage into the model and incorporates practical engineering constraints. The performance of different optimization control strategies for active and reactive power is investigated, and the results indicate that the IEPSO algorithm is efficient for dynamic optimization, and the dynamic contribution rate control strategy for energy storage is more effective than static strategy. [ABSTRACT FROM AUTHOR]

Nowadays some recommender system researchers have already been engaging multi-criteria that model possible attributes of the item to generate the improved recommendations. However, the statistical machine learning methods successful in the single-rating recommender system have not been investigated in the context of multi-criteria ratings. In this paper, we propose two types of multi-criteria probabilistic latent semantic analysis algorithms extended from the single-rating version. First, the mixture of multi-variate Gaussian distribution is assumed to be the underlying distribution of multi-criteria ratings of each user. Second, we further assume the mixture of the linear Gaussian regression model as the underlying distribution of multi-criteria ratings of each user, inspired by the Bayesian network and linear regression. The experiment results on the Yahoo!Movies ratings data set show that the full multi-variate Gaussian model and the linear Gaussian regression model achieve a stable performance gain over other tested methods. [ABSTRACT FROM AUTHOR]