Trust is crucial in dynamic multi-agent systems, where agents may frequently join and leave, and the structure of the society may often change. In these environments, it may be difficult for agents to form stable trust relationships necessary for confident interactions. Societies may break down when trust between agents is too low to motivate interactions. In such settings, agents should make d...

BACKGROUND The Bladder Cancer Index (BCI) is so far the only instrument applicable across all bladder cancer patients, independent of tumor infiltration or treatment applied. We developed a Spanish version of the BCI, and assessed its acceptability and metric properties. METHODS For the adaptation into Spanish we used the forward and back-translation method, expert panels, and cognitive debri...

We study consistency of learning algorithms for a multi-class performance metric that is anon-decomposable function of the confusion matrix of a classifier and cannot be expressed asa sum of losses on individual data points; examples of such performance metrics include themicro and macro F-measure used widely in information retrieval and the multi-class G-meanmetric popular in c...

The h-­‐index can be a useful metric for evaluating a person’s output of Internet media. Here we advocate and demonstrate adaption of the h-­‐index and the g-­‐index to the top video content creators on YouTube. The h-­‐index for Internet video media is based on videos and their view counts. The index h is defined as the number of videos with ≥ h×105 views. The index g is defined as the number ...

We present a general formulation of metric learning for co-embedding, where the goal is to relate objects from different sets. The framework allows metric learning to be applied to a wide range of problems—including link prediction, relation learning, multi-label tagging and ranking—while allowing training to be reformulated as convex optimization. For training we provide a fast iterative algor...

Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent ...

A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind simultaneously predicts four condition metrics concerning pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate conditions make treatme...

We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents degree of similarity pair. In the training stage, model is trained with both and an existing loss function learning, simultaneously; t...

This paper addresses the interference and load imbalance problems in multi-radio infrastructure mesh networks where each mesh node is equipped with multiple radio interfaces and a subset of nodes serve as Internet gateways. To provide backbone support, it is necessary to reduce interference and balance load in Wireless Mesh Networks (WMNs). In this paper, we propose a new Load-Aware Routing Met...