Modern Internet applications rely on rich multimedia contents making the quality of experience (QoE) of end users sensitive to network conditions. Several models were developed in the literature to express QoE as a function of measurements carried out on the traffic of the applications themselves. In this paper, we propose a new methodology based on machine learning able to link expected QoE to network and device level measurements outside the applications' traffic. This direct linking to network and device level measurements is important for the prediction of QoE. We prove the feasibility of the approach in the context of Skype. In particular, we derive and validate a model to predict the Skype QoE as a function of easily measurable network performance metrics. One can see our methodology as a new way of performing measurements in the Internet, where instead of expressing the expected performance in terms of network and device level measurements that only specialists can understand, we express performance in clear terms related to expected quality of experience for different applications.

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Extracting criminals’ information and discovering their network are techniques that investigators often rely on to get extra information about criminal incidents and potential criminals. With the recent advances of the Web, a.k.a. Web 2.0, it has become a rich source of data which provides a variety of information sources. In this article, we propose an integrated framework that combines a variety of available components and makes use of different sources of information provided on the Web to get a better knowledge about criminals or terrorists (we will use criminals to cover all terrorists in the rest of this article). Our system extracts criminals’ information and their corresponding network using Web sources, such as online newspapers, official reports, and social media. It uses text analysis to identify key persons and topics from crawled Web documents. We build a criminal graph from the analysed text based on the co-occurrence of mentioning of criminals. Further analysis is applied on the constructed graph to get key people, hidden relationships and interactions between criminals, as well as hierarchical criminal groups within a network. For every process in the framework, we analysed various available works and implementations that could be used in the process. While analysing social media posts, we identified several challenges which show what solutions could be used for that purpose. Finally, we provide a Web application which implements the proposed framework. It also shows how helpful and efficient the system is in extracting and analysing criminal information.

Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts.