Self-Learning Classifier for Internet traffic

Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. In this paper, we propose SeLeCT, a Self-Learning Classifier for Internet traffic. It uses unsupervised algorithms along with an adaptive learning approach to automatically let classes of traffic emerge, being identified and (easily) labeled. SeLeCT automatically groups flows into pure (or homogeneous) clusters using alternating simple clustering and filtering phases to remove outliers. SeLeCT uses an adaptive learning approach to boost its ability to spot new protocols and applications. Finally, SeLeCT also simplifies label assignment (which is still based on some manual intervention) so that proper class labels can be easily discovered. We evaluate the performance of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents. Our experiments show that SeLeCT achieves overall accuracy close to 98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to help discovering new protocols and applications in an almost automated fashio

The role of parenting styles on behavior problem profiles of adolescents

Parental behavior is one of the most influential factors on the development of adolescent externalizing and internalizing behavior problems. These behavioral problems are closely related and often co-occur. The objectives of this work were: (i) to identify adolescents profiles according to their behavior problems; (ii) to explore individual, family, and social characteristics associated with these profiles; and (iii) to analyze the potential role of parenting styles in belonging to adolescents’ profiles. A total of 449 Spanish adolescents (223 from families declared at-risk and enrolled in Child Welfare Services and 226 from families from the general population) participated in this study. The analyses revealed three profiles of adolescents based on external and internal behavior problems (adjusted, external maladjustment, and internal maladjustment). Parenting styles explained the adolescents’ belonging to different profiles, in which the indulgent style was the most favorable in general terms. The distinctive role of parenting styles on two types of maladjustment profiles was confirmed. The relationship between parenting styles and adolescent adjustment is a key component that should be included in interventions according to adolescents’ behavior problem profiles. Furthermore, the results shed light on the need that family interventions are complemented with individualized interventions with adolescents that accumulate stressful life events.Ministerio de Economía y Competitividad EDU2013-41441-

GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments

Summary: Accurate prediction of transcription factor binding motifs that are enriched in a collection of sequences remains a computational challenge. Here we report on GimmeMotifs, a pipeline that incorporates an ensemble of computational tools to predict motifs de novo from ChIP-sequencing (ChIP-seq) data. Similar redundant motifs are compared using the weighted information content (WIC) similarity score and clustered using an iterative procedure. A comprehensive output report is generated with several different evaluation metrics to compare and evaluate the results. Benchmarks show that the method performs well on human and mouse ChIP-seq datasets. GimmeMotifs consists of a suite of command-line scripts that can be easily implemented in a ChIP-seq analysis pipeline

Seabed characterization: developing fit for purpose methodologies

We briefly describe three methods of seabed characterization which are ‘fit for purpose’, in that each approach is well suited to distinct objectives e.g. characterizing glacial geomorphology and shallow glacial geology vs. rapid prediction of seabed sediment distribution via geostatistics. The methods vary from manual ‘expert’ interpretation to increasingly automated and mathematically based models, each with their own attributes and limitations. We would note however that increasing automation and mathematical sophistication does not necessarily equate to improve map outputs, or reduce the time required to produce them. Judgements must be made to select methodologies which are most appropriate to the variables mapped, and according to the extent and presentation scale of final maps

Three-dimensional interpretation of an imperfect line drawing.

by Leung Kin Lap.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 70-72).ACKNOWLEDGEMENTS --- p.IABSTRACT --- p.IITABLE OF CONTENTS --- p.IIITABLE OF FIGURES --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Contributions of the thesis --- p.2Chapter 1.2 --- Organization of the thesis --- p.4Chapter Chapter 2 --- Previous Work --- p.5Chapter 2.1 --- An overview of 3-D interpretation --- p.5Chapter 2.1.1 --- Multiple-View Clues --- p.5Chapter 2.1.2 --- Single-View Clues --- p.6Chapter 2.2 --- Line Drawing Interpretation --- p.7Chapter 2.2.1 --- Qualitative Interpretation --- p.7Chapter 2.2.2 --- Quantitative Interpretation --- p.10Chapter 2.3 --- Previous Methods of Quantitative Interpretation by Optimization --- p.12Chapter 2.3.1 --- Extremum Principle for Shape from Contour --- p.12Chapter 2.3.2 --- MSDA Algorithm --- p.14Chapter 2.4 --- Comments on Previous Work on Line Drawing Interpretation --- p.17Chapter Chapter 3 --- An Iterative Clustering Procedure for Imperfect Line Drawings --- p.18Chapter 3.1 --- Shape Constraints --- p.19Chapter 3.2 --- Problem Formulation --- p.20Chapter 3.3 --- Solution Steps --- p.25Chapter 3.4 --- Nearest-Neighbor Clustering Algorithm --- p.37Chapter 3.5 --- Discussion --- p.38Chapter Chapter 4 --- Experimental Results --- p.40Chapter 4.1 --- Synthetic Line Drawings --- p.40Chapter 4.2 --- Real Line Drawing --- p.42Chapter 4.2.1 --- Recovery of real images --- p.42Chapter Chapter 5 --- Conclusion and Future Work --- p.65Appendix A --- p.67Chapter A. 1 --- Gradient Space Concept --- p.67Chapter A. 2 --- Shading of images --- p.69Appendix B --- p.7