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An adaptive cooperative dual particle swarm optimization algorithm with chaotic mutation and quantum behavior is proposed to solve the contradiction between global search and local refinement search for basic particle swarm optimization algorithms. The strategy of adaptive cooperative evolution for two subgroups is used to parallel search, the subgroup with the chaotic mutation operator modifies the historical optimal position of particles and the subgroup optimal position using the principle of chaotic randomly ergodicity, and the chaotic mutation radius is increasing with the iterative evolution to enhance the global search ability. Additionally, in order to improve the local refinement search ability, the subgroup with quantum behavior, which casts off the searching orbital, updates the average optimal position of the subgroup and the subgroup optimal position during evolution. Finally, the numerical simulation results demonstrate that the proposed algorithm not only has fast convergence speed and high convergence accuracy, but also has significant advantages in dimension expansion. [ABSTRACT FROM AUTHOR]

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods. [ABSTRACT FROM AUTHOR]

For most mechanistic response analyses of asphalt pavements, the traffic loading between the tire and the pavement is often modeled as a circular contact area with a uniform stress distribution. In reality, the tire-pavement contact pressure is hardly circular or uniformly distributed. In order to more accurately model the pavement responses under traffic loading, it is necessary to measure the actual tire-pavement contact pressure (TPCP) and then use it for pavement mechanistic response analyses. Currently there are a few devices that can be used to measure the TPCP. However, most of these existing devices have some limitations. For example one of the major disadvantages is the fact that the measured data were the pressure between the tire and a steel plate, and not between the tire and the asphalt pavement. For accurate TPCP measurements in this paper, a simple and effective static laboratory test device was developed. One of the features of this device is that the pressure sensors were inserted and suspended within the asphalt slab and the top of the sensors were kept flush with the surface at the interface with the tire. This enabled the true TPCP between the tire and the asphalt surface to be measured. Additionally, the device is relatively easy to set up and conduct the tests. Thus far, the laboratory measured TPCP results with this static test device were reasonably consistent with existing theories and assumptions about modeling the TPCP and pavement mechanistic responses. [ABSTRACT FROM AUTHOR]