Design Architecture-Based on Web Server and Application Cluster in Cloud Environment

Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in web server with multi-level index keys. The web server uses to handle traffic throughput. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators

Influencing HIV treatment success in India : do mobile phones really work?

Background: Sustaining treatment adherence and long-term virological suppression is a global health challenge in HIV management. Mobile phone-based interventions are increasingly harnessed to enhance medication adherence in HIV infection, although supporting evidence for implementation is limited by lack of robust efficacy trials in settings such as India. The overall aim of this thesis was to assess whether customized mobile phone reminders would improve adherence to therapy and thus decrease virological failure among HIV-infected patients initiating anti-retroviral treatment (ART) within the Indian national AIDS control program, and to investigate factors related to the success of this intervention within this population. Methods: To test the feasibility and acceptability of the mobile phone-based reminder system, we conducted a 12-month single center pilot study among 150 HIV-infected ARTexperienced patients (Study I). Subsequently we conducted a two-year randomized controlled trial at three sites in southern India (Bangalore, Mysore and Chennai), where 631 eligible ART-naïve patients were enrolled (Studies II, III, IV). The intervention consisted of weekly interactive voice reminders, along with a weekly pictorial text message for two years. Patients were monitored for pill count adherence measurements, adherence barriers, drug toxicity, CD4 counts and viral load every three months. Results: The results of the pilot study indicated good acceptability and feasibility of the intervention, however a definite beneficial effect on adherence was inconclusive. (Study I). Analysis of the randomized controlled trial revealed no observed statistically significant difference in time to virological failure or sub-optimal adherence (mean adherence <95%) between the intervention and control groups, even after adjusting for potential confounders (Study II). Virological failure was associated with lower adherence levels, non-tenofovir drug regimens and primary drug resistance. Adherence levels and barriers varied significantly over time. The commonly reported barrier, ‘forgetfulness’ was not associated with virological failure. Significant determinants of optimal adherence were older age, higher level of education, greater disclosure status, and patients’ satisfaction with health status, medications and healthcare access (Study III). ART toxicity related to zidovudine and nevirapine was associated with lower levels of adherence, particularly in the first 6 months after ART initiation (Study IV). Conclusions: The results of this thesis indicate that mobile phone-based reminders alone may not improve adherence and promote treatment success among HIV-infected patients. Adherence behavior is a complex dynamic process with a multitude of diverse influencing factors. Optimal adherence and treatment success may be better sustained by minimizing drug interruptions for medical reasons, use of safer first-line ART regimens, and strengthening both patient self-efficacy and patient-health provider relationships

Effect of Drug Loading Method and Drug Physicochemical Properties on the Material and Drug Release Properties of Poly (Ethylene Oxide) Hydrogels for Transdermal Delivery

Novel poly (ethylene oxide) (PEO) hydrogel films were synthesized via UV cross-linking with pentaerythritol tetra-acrylate (PETRA) as cross-linking agent. The purpose of this work was to develop a novel hydrogel film suitable for passive transdermal drug delivery via skin application. Hydrogels were loaded with model drugs (lidocaine hydrochloride (LID), diclofenac sodium (DIC) and ibuprofen (IBU)) via post-loading and in situ loading methods. The effect of loading method and drug physicochemical properties on the material and drug release properties of medicated film samples were characterized using scanning electron microscopy (SEM), swelling studies, differential scanning calorimetry (DSC), fourier transform infrared spectroscopy (FT-IR), tensile testing, rheometry, and drug release studies. In situ loaded films showed better drug entrapment within the hydrogel network and also better polymer crystallinity. High drug release was observed from all studied formulations. In situ loaded LID had a plasticizing effect on PEO hydrogel, and films showed excellent mechanical properties and prolonged drug release. The drug release mechanism for the majority of medicated PEO hydrogel formulations was determined as both drug diffusion and polymer chain relaxation, which is highly desirable for controlled release formulation

A Probabilistic Logic Programming Event Calculus

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journa

High viremia and low level of transmitted drug resistance in anti-retroviral therapy-naïve perinatally-infected children and adolescents with HIV-1 subtype C infection

BACKGROUND: High plasma viremia in HIV-1 infection is associated with rapid CD4 cell decline and faster disease progression. Children with HIV infection have high viral loads, particularly in early childhood. In this study we sought to understand the relationship between duration of HIV-1 infection and viral dynamics among perinatally-infected children and adolescents in India along with transmitted drug resistance in this population. METHODS: During 2007–2011, cross-sectional samples were collected from ART-naïve children (n = 105) with perinatally-acquired HIV infection, aged 2–16 years from Bangalore, India. CD4 counts, viral load and in-house genotyping were performed and transmitted drug resistance mutations were identified using the World Health Organization recommendations for Surveillance of Drug Resistance Mutations (SDRM_2009) list. RESULTS: Among 105 children studied, 73.3% (77/105) were asymptomatic, but had a median viral load of 5.24 log copies/mL (IQR 4.62-5.66). In the adolescent age group, 54% (21/39) had high levels of viremia (median 5.14 log copies/mL) but were asymptomatic. HIV-1 subtyping identified 98% strains (103/105) as subtype C; one A1 and one unique recombinant form (URF). Transmitted NRTI resistance was 1.9% (2/105); NNRTI resistance was 4.8% (5/105) and overall prevalence of transmitted drug resistance was 5.7% (6/105). CONCLUSIONS: The high burden of plasma viremia found among untreated asymptomatic adolescents needs to be addressed both from an individual angle to halt disease progression, and from a public health perspective to arrest horizontal transmission. The low level of transmitted drug resistance among perinatally-infected children is reassuring; however with improving ART access globally, regular genotyping surveillance is indicated