Characterizing eve: Analysing cybercrime actors in a large underground forum

Underground forums contain many thousands of active users, but the vast majority will be involved, at most, in minor levels of deviance. The number who engage in serious criminal activity is small. That being said, underground forums have played a significant role in several recent high-profile cybercrime activities. In this work we apply data science approaches to understand criminal pathways and characterize key actors related to illegal activity in one of the largest and longest- running underground forums. We combine the results of a logistic regression model with k-means clustering and social network analysis, verifying the findings using topic analysis. We identify variables relating to forum activity that predict the likelihood a user will become an actor of interest to law enforcement, and would therefore benefit the most from intervention. This work provides the first step towards identifying ways to deter the involvement of young people away from a career in cybercrime.Alan Turing Institut

Prevalence and predictors of video game addiction: a study based on a national representative sample of gamers

Video gaming has become a popular leisure activity in many parts of the world, and an increasing number of empirical studies examine the small minority that appears to develop problems as a result of excessive gaming. This study investigated prevalence rates and predictors of video game addiction in a sample of gamers, randomly selected from the National Population Registry of Norway (N =3389). Results showed there were 1.4 % addicted gamers, 7.3 % problem gamers, 3.9 % engaged gamers, and 87.4 % normal gamers. Gender (being male) and age group (being young) were positively associated with addicted-, problem-, and engaged gamers. Place of birth (Africa, Asia, South- and Middle America) were positively associated with addicted- and problem gamers. Video game addiction was negatively associated with conscientiousness and positively associated with neuroticism. Poor psychosomatic health was positively associated with problem- and engaged gaming. These factors provide insight into the field of video game addiction, and may help to provide guidance as to how individuals that are at risk of becoming addicted gamers can be identified

Neurobehavioral Deficits and Increased Blood Pressure in School-Age Children Prenatally Exposed to Pesticides

Background: The long-term neurotoxicity risks caused by prenatal exposures to pesticides are unclear, but a previous pilot study of Ecuadorian school children suggested that blood pressure and visuospatial processing may be vulnerable. Objectives: In northern Ecuador, where floriculture is intensive and relies on female employment, we carried out an intensive cross-sectional study to assess children’s neurobehavioral functions at 6–8 years of age. Methods: We examined all 87 children attending two grades in the local public school with an expanded battery of neurobehavioral tests. Information on pesticide exposure during the index pregnancy was obtained from maternal interview. The children’s current pesticide exposure was assessed from the urinary excretion of organophosphate metabolites and erythrocyte acetylcholine esterase activity. Results: Of 84 eligible participants, 35 were exposed to pesticides during pregnancy via maternal occupational exposure, and 23 had indirect exposure from paternal work. Twenty-two children had detectable current exposure irrespective of their prenatal exposure status. Only children with prenatal exposure from maternal greenhouse work showed consistent deficits after covariate adjustment, which included stunting and socioeconomic variables. Exposure-related deficits were the strongest for motor speed (Finger Tapping Task), motor coordination (Santa Ana Form Board), visuospatial performance (Stanford-Binet Copying Test), and visual memory (Stanford-Binet Copying Recall Test). These associations corresponded to a developmental delay of 1.5–2 years. Prenatal pesticide exposure was also significantly associated with an average increase of 3.6 mmHg in systolic blood pressure and a slight decrease in body mass index of 1.1 kg/m2. Inclusion of the pilot data strengthened these results. Conclusions: These findings support the notion that prenatal exposure to pesticides—at levels not producing adverse health outcomes in the mother—can cause lasting adverse effects on brain development in children. Pesticide exposure therefore may contribute to a “silent pandemic” of developmental neurotoxicity

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Regression toward the mean – a detection method for unknown population mean based on Mee and Chua's algorithm

<p>Abstract</p> <p>Background</p> <p>Regression to the mean (RTM) occurs in situations of repeated measurements when extreme values are followed by measurements in the same subjects that are closer to the mean of the basic population. In uncontrolled studies such changes are likely to be interpreted as a real treatment effect.</p> <p>Methods</p> <p>Several statistical approaches have been developed to analyse such situations, including the algorithm of Mee and Chua which assumes a known population mean <it>μ</it>. We extend this approach to a situation where <it>μ </it>is unknown and suggest to vary it systematically over a range of reasonable values. Using differential calculus we provide formulas to estimate the range of <it>μ </it>where treatment effects are likely to occur when RTM is present.</p> <p>Results</p> <p>We successfully applied our method to three real world examples denoting situations when (a) no treatment effect can be confirmed regardless which <it>μ </it>is true, (b) when a treatment effect must be assumed independent from the true <it>μ </it>and (c) in the appraisal of results of uncontrolled studies.</p> <p>Conclusion</p> <p>Our method can be used to separate the wheat from the chaff in situations, when one has to interpret the results of uncontrolled studies. In meta-analysis, health-technology reports or systematic reviews this approach may be helpful to clarify the evidence given from uncontrolled observational studies.</p

Explorative visual analytics on interval-based genomic data and their metadata

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSEunder GPLv3 open-source license

Replication of CNTNAP2 association with nonword repetition and support for FOXP2 association with timed reading and motor activities in a dyslexia family sample

Two functionally related genes, FOXP2 and CNTNAP2, influence language abilities in families with rare syndromic and common nonsyndromic forms of impaired language, respectively. We investigated whether these genes are associated with component phenotypes of dyslexia and measures of sequential motor ability. Quantitative transmission disequilibrium testing (QTDT) and linear association modeling were used to evaluate associations with measures of phonological memory (nonword repetition, NWR), expressive language (sentence repetition), reading (real word reading efficiency, RWRE; word attack, WATT), and timed sequential motor activities (rapid alternating place of articulation, RAPA; finger succession in the dominant hand, FS-D) in 188 family trios with a child with dyslexia. Consistent with a prior study of language impairment, QTDT in dyslexia showed evidence of CNTNAP2 single nucleotide polymorphism (SNP) association with NWR. For FOXP2, we provide the first evidence for SNP association with component phenotypes of dyslexia, specifically NWR and RWRE but not WATT. In addition, FOXP2 SNP associations with both RAPA and FS-D were observed. Our results confirm the role of CNTNAP2 in NWR in a dyslexia sample and motivate new questions about the effects of FOXP2 in neurodevelopmental disorders

In Search of Cellular Immunophenotypes in the Blood of Children with Autism

Autism is a neurodevelopmental disorder characterized by impairments in social behavior, communication difficulties and the occurrence of repetitive or stereotyped behaviors. There has been substantial evidence for dysregulation of the immune system in autism.We evaluated differences in the number and phenotype of circulating blood cells in young children with autism (n = 70) compared with age-matched controls (n = 35). Children with a confirmed diagnosis of autism (4-6 years of age) were further subdivided into low (IQ<68, n = 35) or high functioning (IQ ≥ 68, n = 35) groups. Age- and gender-matched typically developing children constituted the control group. Six hundred and forty four primary and secondary variables, including cell counts and the abundance of cell surface antigens, were assessed using microvolume laser scanning cytometry.There were multiple differences in immune cell populations between the autism and control groups. The absolute number of B cells per volume of blood was over 20% higher for children with autism and the absolute number of NK cells was about 40% higher. Neither of these variables showed significant difference between the low and high functioning autism groups. While the absolute number of T cells was not different across groups, a number of cellular activation markers, including HLA-DR and CD26 on T cells, and CD38 on B cells, were significantly higher in the autism group compared to controls.These results support previous findings that immune dysfunction may occur in some children with autism. Further evaluation of the nature of the dysfunction and how it may play a role in the etiology of autism or in facets of autism neuropathology and/or behavior are needed