Bipartite graphs are widely used to capture the relationships between two types of entities. In bipartite graph analysis, finding the maximum balanced biclique (MBB) is an important problem with numerous applications. A biclique is balanced if its two disjoint vertex sets are of equal size. However, in real-world scenarios, each vertex is associated with a weight to denote its properties, such as influence, i.e., weighted bipartite graph. For weighted bipartite graphs, the previous studies for MBB are no longer applicable due to the ignorance of weight. To fill the gap, in this paper, we propose a reasonable definition of “balance” by restricting the weight difference between two sides of a biclique within $k$. Given a weighted bipartite graph $G$ and a constraint $k$, we aim to find the maximum $k$-balanced biclique (Max $k$ BB) with the maximum weight. To address the problem, we first propose an approach based on biclique enumeration on single side of $G$ following the Branch-and
Abstract
Multi-agent technologies have been widely applied to many applications, such as in e-markets, cloud computing, service-oriented environments, etc. In real applications, service-oriented environments are open and dynamic, where loosely coupled agents interact to consume and provide services. How to accurately evaluate the potential performance (i.e., QoS) of service providers on the service requested by a service consumer in such open and dynamic environments is a challenging issue in both theory and practice. In this paper, an innovative approach is proposed to evaluate the QoS of service providers in service-oriented environments. The proposed approach first borrows the reference report mechanism from the certified reputation model, so as to efficiently collect reference reports (i.e., historical performance) of service providers in open and dynamic environments. Then, a tensor-based QoS model is proposed to construct multi-dimensional relationships between QoS evaluation
Abstract
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the perfor
Abstract
Young people s digital lives are bigger than they have ever been. This means that realizing young people s rights now requires a concerted focus on the digital world as well. Terms and Conditions (from Cookies Policies to Terms of Service) are an important part of young people s digital worlds because they set the ‘rules of engagement’ between digital products and young service users. However important, these Terms and Conditions rarely recognize young people s rights, let alone uphold them. This article outlines some of the ways Terms and Conditions fail young people, and why this is problematic from moral, legal and commercial perspectives. This suggests there is a critical need for Terms and Conditions that uphold rights, and that a Standard around Terms and Conditions may be an effective way of addressing this problem.