Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

Enhancing random forests performance in microarray data classification

Random forests are receiving increasing attention for classification of microarray datasets. We evaluate the effects of a feature selection process on the performance of a random forest classifier as well as on the choice of two critical parameters, i.e. the forest size and the number of features chosen at each split in growing trees. Results of our experiments suggest that parameters lower than popular default values can lead to effective and more parsimonious classification models. Growing few trees on small subsets of selected features, while randomly choosing a single variable at each split, results in classification performance that compares well with state-of-art studies

Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement

BioCloud Search EnGene: Surfing Biological Data on the Cloud

The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple “Google-like” query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/

Catalunya a l'aldea global

Small societies must also adapt to an increasingly integrated world, econmmically, culturally and politically. Globalization is a polifaceted phenomenon transforming the world into a global village. Three examples of globalisation are the rapidly extended financial crisis, with the threat of global recession; the arrest of Pinochet in London on a Spanish warrant, marking the birth of a global public opinion; and the acceptance of differences as a way to solve conflicts both in Northern Ireland and in the creation of the European Union. Globalisation is forcing societies to adapt their cultural, institutional and political references. The two biggest transformations of the 20th Century have been continued economic growth and the political preeminence of democracy, which are interrelated. Catalan society has made a fundamental contribution to both in Spain. This is no guarantee for the future with globalisation. The complexity of modern society is based on the 17th Century principle of tolerance. Descentralisation promotes the natural process of self-government, but globalisation makes universal problems difficult to solve in a limited territory. The political union of Europe is the alternative. Prosperity is not the result of natural advantages, but of values which favour productivity. These values respect individual freedom. A business culture does not arise from business schools but from these values of democracy and tolerance. The role of the public sector is to guarantee social cohesion, efficient education and adequate infrastructures, including electronic communications

Fostering innovation in library management and leadership: The University of Hong Kong libraries leadership institute

Purpose - The purpose of this paper is to discuss experiences gained from the introduction of a library leadership institute for Asian academic librarians. Design/methodology/approach - The success of the institute is measured through the evaluations of all participants including, most recently, an attempt to identify challenges faced by academic library leaders, and potential leaders, and assessing how well the institute addresses those challenges. Findings - While evaluations of the institute are highly positive, there appears to be potential for expanding the institute into two streams, one being strictly leadership and the other drawing mainly on management issues. Research limitations/implications - While analysis of institute evaluations and comments demonstrates a great deal of satisfaction, further research should be undertaken to identify long-term benefits gained by participants. Practical implications - The volatile world of information places many challenges on library leaders in the Asia region. The need for strong leadership is apparent as librarians must draw on a range of skills that are not traditionally taught in library schools and are often difficult to develop in the workplace. The benefits of leadership institutes, while limited, do at least plant a seed for new ideas and ways of thinking. Originality/value - The paper provides a through analysis of the only Asian academic library leadership institute. It is useful for others considering establishing a similar institute or for those concerned with library professional development in Asia.postprin

Mise en évidence d’une phase tectonique au Santonien du versant Nord du Haut Atlas Occidental, Maroc

At the level of the north hillside of the western High Atlas, only the training of limestones and dolomitic marls of Aït Abbes was recognized in Senonian. It is topped by the phosphated series. To highlight a tectonic action at the end of lower Santonian of Sidi Bou Othman's region and which is probably due to a halocinique phase, three cuts were been lifted on both sides of the Assif Aït Tabgaw. This tectonic phase appears by angular unconformity within the deposits of the training of Aït Abbes, microfaults, synsedimentary slidings, and monogenic breaches… It also structures the whole region at the high bottom or emergent low lands forming several small basins confined and supersaturated in brines. Then, following an important peneplanation, a platform of sebkha type is set up on the whole region. Some brief marine incursions reach the eastern basins

Learning from high-dimensional and class-imbalanced datasets using random forests

Class imbalance and high dimensionality are two major issues in several real-life applications, e.g., in the fields of bioinformatics, text mining and image classification. However, while both issues have been extensively studied in the machine learning community, they have mostly been treated separately, and little research has been thus far conducted on which approaches might be best suited to deal with datasets that are class-imbalanced and high-dimensional at the same time (i.e., with a large number of features). This work attempts to give a contribution to this challenging research area by studying the effectiveness of hybrid learning strategies that involve the integration of feature selection techniques, to reduce the data dimensionality, with proper methods that cope with the adverse effects of class imbalance (in particular, data balancing and cost-sensitive methods are considered). Extensive experiments have been carried out across datasets from different domains, leveraging a well-known classifier, the Random Forest, which has proven to be effective in high-dimensional spaces and has also been successfully applied to imbalanced tasks. Our results give evidence of the benefits of such a hybrid approach, when compared to using only feature selection or imbalance learning methods alone