A Two-stage Flow-based Intrusion Detection Model ForNext-generation Networks

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results

Prevalence and demographics of anxiety disorders: a snapshot from a community health centre in Pakistan

<p>Abstract</p> <p>Background</p> <p>The developing world is faced with a high burden of anxiety disorders. The exact prevalence of anxiety disorders in Pakistan is not known. There is a need to develop an evidence base to aid policy development on tackling anxiety and depressive disorders in the country. This is the first pilot study to address the prevalence of anxiety disorders and their association with sociodemographic factors in Pakistan.</p> <p>Methods</p> <p>A cross-sectional study was conducted among people visiting Aga Khan University Hospital (AKUH), a tertiary care facility in Karachi, Pakistan. The point prevalence of anxiety amongst the sample population, which comprised of patients and their attendants, excluding all health care personnel, was assessed using the validated Urdu version of the Hospital Anxiety and Depression Scale (HADS). The questionnaire was administered to 423 people. Descriptive statistics were performed for mean scores and proportions.</p> <p>Results</p> <p>The mean anxiety score of the population was 5.7 ± 3.86. About 28.3% had borderline or pathological anxiety. The factors found to be independently predicted with anxiety were, female sex (odds ratio (OR) = 2.14, 95% CI 1.36–3.36, p = 0.01); physical illness (OR = 1.67, 95% CI 1.06–2.64, p = 0.026); and psychiatric illness (OR = 1.176, 95% CI 1.0–3.1, p = 0.048). In the final multivariate model, female sex (adjusted odds ratio (AOR) = 2, 95% CI 1.28–3.22) and physical illness (AOR = 1.56, 95% CI 0.97–2.48) were found to be significant.</p> <p>Conclusion</p> <p>Further studies via nationally representative surveys need to be undertaken to fully grasp the scope of this emerging public health issue in Pakistan.</p

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

The effects of material formulation and manufacturing process on mechanical and thermal properties of epoxy/clay nanocomposites

A holistic study was conducted to investigate the combined effect of three different pre-mixing processes, namely mechanical mixing, ultrasonication and centrifugation, on mechanical and thermal properties of epoxy/clay nanocomposites reinforced with different platelet-like montmorillonite (MMT) clays (Cloisite Na+, Cloisite 10A, Cloisite 15 or Cloisite 93A) at clay contents of 3–10 wt%. Furthermore, the effect of combined pre-mixing processes and material formulation on clay dispersion and corresponding material properties of resulting composites was investigated using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), flexural and Charpy impact tests, Rockwell hardness tests and differential scanning calorimetry (DSC). A high level of clay agglomeration and partially intercalated/exfoliated clay structures were observed regardless of clay type and content. Epoxy/clay nanocomposites demonstrate an overall noticeable improvement of up to 10 % in the glass transition temperature (Tg) compared to that of neat epoxy, which is interpreted by the inclusion of MMT clays acting as rigid fillers to restrict the chain mobility of epoxy matrices. The impact strength of epoxy/clay nanocomposites was also found to increase by up to 24 % with the addition of 3 wt% Cloisite Na+ clays. However, their flexural strength and hardness diminished when compared to those of neat epoxy, arising from several effects including clay agglomeration, widely distributed microvoids and microcracks as well as weak interfacial bonding between clay particles and epoxy matrices, as confirmed from TEM and SEM results. Overall, it is suggested that an improved technique should be used for the combination of pre-mixing processes in order to achieve the optimal manufacturing condition of uniform clay dispersion and minimal void contents

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Observation of the diphoton decay of the Higgs boson and measurement of its properties

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Study of double parton scattering using W+2-jet events in proton-proton collisions at √s=7 TeV

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Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV

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Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

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