When I create an application, I usually want to include an “About” box to let the user know more about the application and me, and to give shout-outs to anyone who may have helped in the creation of my program. One cool feature wxPython provides is a custom AboutBox widget. I think it looks a little odd, so I created my own About box using the HtmlWindow widget. However, I’ll show how to do it both ways in this recipe.

Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each data source has an associated ontology. Different users or applications need to be able to query such data sources for statistics of interest (e.g., statistics needed to learn a predictive model from data). Because no single ontology meets the needs of all applications or users in every context, or for that matter, even a single user in different contexts, there is a need for principled approaches to acquiring statistics from semantically heterogeneous data. In this paper, we introduce ontology-extended data sources and define a user perspective consisting of an ontology and a set of interoperation constraints between data source ontologies and the user ontology. We show how these constraints can be used to derive mappings from source ontologies to the user ontology. We observe that most of the learning algorithms use only certain statistics computed from data in the process of generating the hypothesis that they output. We show how the ontology mappings can be used to answer statistical queries needed by algorithms for learning classifiers from data viewed from a certain user perspective.

Failures of traditional survey methods for measuring political climate and forecasting high impact events such as elections, offers opportunities to seek alternative methods. The analysis of social networks with computational linguistic methods have been proved to be useful as an alternative, but several studies related to these areas were conducted after the event (post hoc). Since 2017 was the election year for the 2018–2022 period for Chile and, moreover, there were three instances of elections in this year. This condition makes a good environment to conduct a case study for forecasting these elections with the use of social media as the main source of Data. This paper describes the implementation of multiple algorithms of supervised machine learning to do political sentiment analysis to predict the outcome of each election with Twitter data. These algorithms are Decision Trees, AdaBoost, Random Forest, Linear Support Vector Machines and ensemble voting classifiers. Manual annotations of a training set are conducted by experts to label pragmatic sentiment over the tweets mentioning an account or the name of a candidate to train the algorithms. Then a predictive set is collected days before the election and an automatic classification is performed. Finally the distribution of votes for each candidate is obtained from this classified set on the positive sentiment of the tweets. Ultimately, an accurate prediction was achieved using an ensemble voting classifier with a Mean Absolute Error of $$0.51\%$$ for the second round.

Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the usage of labeled data to generate and optimize initial cluster centers for k-means algorithm. It proposes a max-distance search approach in order to find some optimal initial cluster centers from unlabeled data, especially when labeled data can’t provide enough initial cluster centers. Experimental results demonstrate the advantages of this method over standard random selection and partial random selection, in which some initial cluster centers come from labeled data while the other come from unlabeled data by random selection.

CSMA/ECA is a modification of CSMA/CA that delivers higher performance by reducing the number of collisions. This paper presents an algorithm to adapt a configuration parameter of the protocol to ensure high performance for any given number of competing stations. Performance is measured in terms of efficiency and fairness. The proposed algorithm has been tested by means of simulation to show that it takes half a second for the system to re-adjust the configuration and suppress the collisions when the number of contending stations suddenly increases from zero to twenty.

Based on source of tracing or correlation information, the traceback and correlation approaches can be divided into two categories: host-based and network-based. Host-based approaches rely on information collected at the hosts that are used for stepping stones. Such information includes user login activity, new arrival of connections and new initiation of connections to other hosts. Network-based approaches use some characteristics of network connections and exploit the property of network connections: the essence or semantics of the application level content of connections is invariant across stepping stones.

In this paper we present and evaluate a simple but effective machine learning algorithm that we call Bitvector Machine: Feature vectors are partitioned along component-wise quantiles and converted into bitvectors that are learned. It is shown that the method is efficient in both training and classification. The effectiveness of the method is analysed theoretically for best and worst-case scenarios. Experiments on high-dimensional synthetic and real world data show a huge speed boost compared to Support Vector Machines with RBF kernel. By tabulating kernel functions, computing medians in linear-time, and exploiting modern processor technology for advanced bitvector operations, we achieve a speed-up of 32 for classification and 48 for kernel evaluation compared to the popular LIBSVM. Although the method does not generally outperform a SVM with RBF kernel it achieves a high classification accuracy and has qualitative advantages over the linear classifier.