The vast volumes of open data pose a challenge for users in finding relevant datasets. To address this, we developed a hybrid dataset recommendation model that combines content-based similarity with item-to-item co-occurrence. The features used by the recommender include dataset properties and usage statistics. In this paper, we focus on fine-tuning the weights of these features. We experimenta...

The conventional method of aggregating the satisfaction of transport projects with respect to multiple attributes is commonly some variant of Simple Additive Weighting (SAW), which involves the sum of products of standardized outcomes of projects with respect to attributes and attribute importance weights. It is suggested that alternative forms of aggregation might be more useful, in particular...

Support Vector Machines(SVMs) have succeeded in many classification fields. Some researchers have tried to apply SVMs to Intrusion Detection recently and got desirable results. By analyzing C-SVM theoretically and experimentally, we found that C-SVM had some properties which showed C-SVM was not most suitable for Network Intrusion Detection. First, C-SVM has different classification error rates...

The Metropolis–Hastings algorithm is one of the most basic and well-studied Markov chain Monte Carlo methods. It generates a Markov chain which has as limit distribution the target distribution by simulating observations from a different proposal distribution. A proposed value is accepted with some particular probability otherwise the previous value is repeated. As a consequence, the accepted v...

Multicriteria decision support methods are common in engineering design. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. It has long been known that a weighted sum, when used for multicriteria optimization, may fail to locate all points on a nonconvex Pareto frontier. More recent results from the optimization literature rel...

objectives: the objective of this study is to evaluate the safety climate as an important part of macroergonomics domains and to determine the importance of each safety climate factor in an iranian company. methods: for data gathering, the researchers used macroergonomic organizational questionnaire survey (moqs) method. for conducting this method we applied safety climate questionnaire which h...

in the early stages of a construction project, the reliability and accuracy of conceptual cost estimates are major concerns for clients and cost engineers. previous studies applied scoring methods and established common rules or mathematical methods to assess the quality of cost estimates. however, those approaches have some limitations in adapting to real-world projects or require understandin...

‎Selecting the most suitable robot among their wide range of specifications and capabilities is an important issue to perform the hazardous and repetitive jobs‎. ‎Companies should take into consideration powerful group decision-making (GDM) methods to evaluate the candidates or potential robots versus the selected attributes (criteria)‎. ‎In this study‎, ‎a new GDM method is proposed by utilizi...

The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of...