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

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

Peer reviewe

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

Peer reviewe

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at √s=8 Tev

Peer reviewe

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

Peer reviewe

Measurement of Higgs boson production and properties in the WW decay channel with leptonic final states

Peer reviewe

Measurements of the tt¯ charge asymmetry using the dilepton decay channel in pp collisions at √s=7 TeV

Peer reviewe

Measurement of the production cross section ratio σ(χb2(1P))/σ(χb1(1P))in pp collisions at √s=8TeV

A measurement of the production cross section ratio σ(χb2(1P))/σ(χb1(1P))σ(χb2(1P))/σ(χb1(1P)) is presented. The χb1(1P)χb1(1P) and χb2(1P)χb2(1P) bottomonium states, promptly produced in pp collisions at View the MathML sources=8 TeV, are detected by the CMS experiment at the CERN LHC through their radiative decays χb1,2(1P)→ϒ(1S)+γχb1,2(1P)→ϒ(1S)+γ. The emitted photons are measured through their conversion to e+e−e+e− pairs, whose reconstruction allows the two states to be resolved. The ϒ(1S)ϒ(1S) is measured through its decay to two muons. An event sample corresponding to an integrated luminosity of 20.7 fb−120.7 fb−1 is used to measure the cross section ratio in a phase-space region defined by the photon pseudorapidity, |ηγ|<1.0|ηγ|<1.0; the ϒ(1S)ϒ(1S) rapidity, |yϒ|<1.5|yϒ|<1.5; and the ϒ(1S)ϒ(1S) transverse momentum, View the MathML source7<pTϒ<40 GeV. The cross section ratio shows no significant dependence on the ϒ(1S)ϒ(1S) transverse momentum, with a measured average value of View the MathML source0.85±0.07(stat+syst)±0.08(BF), where the first uncertainty is the combination of the experimental statistical and systematic uncertainties and the second is from the uncertainty in the ratio of the χbχb branching fractions