Estimating Trust Strength For Supporting Effective Recommendation Services

In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend products that match users’ preferences and requirements. In addition to the well-known collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one. The reason is that most of online communities allow users to express their trust on other users. Based on the analysis of trust relationships, the trust-based recommendation approach finds out and consults the opinions of more reliable users and therefore makes better recommendations. Existing trust-based recommendation techniques consider all trust relationships in a given trust network equally important and give them the same trust strength. However, in a real-world setting, trust relationships may be of various strengths. In response, in this study, we propose a mechanism for trust strength estimation on the basis of the machine learning approach and estimate the trust strength for each existing trust relationship in a given trust network. To overcome the sparsity of the trust network, we also develop a modified trust propagation method to expand the original trust network. Finally, we perform a series of experiments to demonstrate the performance of our trust-based recommendation approach based on the trust strength estimation mechanism. Our empirical evaluation results show that our proposed approach outperforms our benchmark techniques, i.e., the traditional collaborative filtering approach and the original trust-based one

Design earthquake ground motion prediction for Perth metropolitan area with microtremor measurements for site characterization

Perth is the largest city in Western Australia and home to three-quarters of the state\u27s residents. In recent decades, there have been a lot of earthquake activities just east of Perth in an area known as the South-West Seismic Zone. Previous numerical results of site response analyses based on limited available geology information for PMA indicated that Perth Basin might amplify the bedrock motion by more than 10 times at some frequencies and at some sites. Hence, more detailed studies on site characterization and amplification are necessary. The microtremor method using spatial autocorrelation (SPAC) processing is a useful tool for gaining thickness and shear wave velocity (SWV) of sediments and has been adopted in many previous studies. In this study, the response spectrum of rock site corresponding to the 475-year return period for PMA is defined according to the probabilistic seismic hazard analysis (PSHA) based on the latest ground motion attenuation model of Southwest Western Australia. Site characterization in PMA is performed using two microtremor measurements, namely SPAC technique and H/V method. The clonal selection algorithm (CSA) is introduced to perform direct inversion of SPAC curves to determine the soil profiles of representative PMA sites investigated in this study. Using the simulated bedrock motion as input, the responses of the soil sites are estimated using numerical method based on the shear-wave velocity vs. depth profiles determined from the SPAC technique. The response spectrum of the earthquake ground motion on surface of each site is derived from the numerical results of the site response analysis, and compared with the respective design spectrum defined in the Australian Earthquake Loading Code. The comparison shows that the code spectra are conservative in the short period range, but may slightly underestimate the response spectrum at some long period range. <br /

Optical and Gamma-Ray Variability Behaviors of 3C 454.3 from 2006 to 2011

We present our photometric monitoring of a flat spectrum radio quasar (FSRQ) 3C 454.3 at Yunnan observatories from 2006 to 2011. We find that the optical color of 3C 454.3 shows obvious redder-when-brighter trend, which reaches a saturation stage when the source is brighter than 15.15 mag at V band. We perform a simulation with multiple values of disk luminosity and spectral index to reproduce the magnitude-color diagram. The results show that the contamination caused by the disk radiation alone is difficult to produce the observed color variability. The variability properties during the outburst in December 2009 are also compared with $\gamma$-ray data derived from Fermi $\gamma$-ray space telescope. The flux variation of these two bands follow a linear relation with $F_{\gamma} \propto F_R^{1.14\pm0.07}$, which provides an observational evidence for external Compton process in 3C 454.3. Meanwhile, this flux correlation indicates that electron injection is the main mechanism for variability origin. We also explore the variation of the flux ratio $F_{\gamma}/F_R$ and the detailed structures in the lightcurves, and discuss some possible origins for the detailed variability behaviors.Comment: accepted for publication in The Astrophysical Journal, 5 figures, 2 table

Dynamic match kernel with deep convolutional features for image retrieval

For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs. To tackle this problem, we propose to construct a dynamic match kernel by adaptively calculating the matching thresholds between query and candidate images based on the pairwise distance among deep CNN features. In contrast to the typical static match kernel which is independent to the global appearance of retrieved images, the dynamic one leverages the semantical similarity as a constraint for determining the matches. Accordingly, we propose a semantic-constrained retrieval framework by incorporating the dynamic match kernel, which focuses on matched patches between relevant images and filters out the ones for irrelevant pairs. Furthermore, we demonstrate that the proposed kernel complements recent methods, such as hamming embedding, multiple assignment, local descriptors aggregation, and graph-based re-ranking, while it outperforms the static one under various settings on off-the-shelf evaluation metrics. We also propose to evaluate the matched patches both quantitatively and qualitatively. Extensive experiments on five benchmark data sets and large-scale distractors validate the merits of the proposed method against the state-of-the-art methods for image retrieval

Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation

Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it is more imperative to provide ultra-dense LEO constellation with enhanced on-board computation capability. Benefited from it, we have proposed a collaborative federated learning over LEO satellite constellation (FedLEO). We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The GS initially selects an LEO server, whereas its LEO clients are all determined by clustering mechanism and communication capability through the optical inter-satellite links (ISLs). The re-clustering of changing LEO server will be executed once with low communication quality of FedLEO. In the simulations, we have numerically analyzed the proposed FedLEO under practical Walker-based LEO constellation configurations along with MNIST training dataset for classification mission. The proposed FedLEO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing

Real-time simulation dynamics model and solution algorithm for the trolley-hoisting system in container crane simulated training system

The development of simulated training system (simulator) for container crane has made some progress; however, there are still problems in insufficient training function (e.g. the container spreader alignment skill training, one of the most important skill in conventional terminals) and lack of dynamic sense of immersion. In this paper, the technical status of container crane simulator is summarized and the state of art of dynamics model and its solution algorithm for container crane is reviewed. It is pointed out that establishing an accurate real-time simulation dynamics model and studying an efficient algorithm under certain calculation accuracy is the key problem of enhancing immersion, reality and training effect of the simulator. With reasonable simplification and hypothesis, the dynamic equilibrium equations of the trolley-hoisting system are established, further considering the characteristics of the mechanical and electrical transmission system of the crane and also the external mean wind load. Based on the four order Runge-Kutta method by MATLAB programming, the fast solution to the two order ordinary differential equations is realized on personal computer, and the three dimensional (3D) space swing time-history response of the container spreader can be obtained in real-time. The results of numerical calculation are consistent with the actual situation, thus, this study provides a feasible technical route for the real-time dynamics simulation in the container crane simulated training system