Reputation systems have become an important means to help users build trust, reduce information asymmetry and filter information in the context of online services provision. Different users cannot rate services under the same criteria due to the scale and dynamism of these systems. Thus, aggregating cardinal ratings into reputation will potentially lead to unreliable and misleading result, which makes reputation systems necessarily consider the impossibility of interpersonal utility comparisons. In this paper, we exploit the ordinal user preferences between services to compute reputation of services. A distance metric is defined to measure the discrepancy between two rating vectors and the reputation computation problem was formalized as an optimization problem. Then, genetic algorithm is used to solve the optimization problem to find a reputation vector that minimizes the total number of disagreements with the rating matrix. We conduct a comprehensive experimental study and performance analysis to evaluate the effectiveness and efficiency of the proposed method.
This paper is to compute a Nash equilibrium in a fuzzy environment, which is represented by a fuzzy approximate Nash equilibrium in a space of discrete mixed strategies. For discrete mixed strategies, the relationship between the discrete degree and the approximate degree is discussed. Based on the fuzzy regret degree, a genetic algorithm for computing a fuzzy Nash equilibrium is given.
The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize non-trivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic "fingerprints" of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing.
An Internet based remote monitor and control system was developed for a high-power solid-state laser-processing systems.It incorporated a new web frame for the products.The newly designed system features high efficiency,accuracy and real-time performance.An advanced software architecture was adopted in the design.The operation status and parameters of the laser-processing system can be remotely monitored,the current data and history data of the laser-processing system can be inquired,some of the data can be modified if authorized.Questions could be answered via on line communication.
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