Higher wages for relief work can make many of the poor worse off : recent evidence from Maharashtra's"Employment Guarantee Scheme"

Among developing countries, the"Employment Guarantee Scheme"(EGS) in the state of Maharashtra in India is probably the most famous and most successful direct governmental effort at reducing absolute poverty in rural areas. Since the mid-1970s, EGS has aimed to offer unskilled rural employment on demand. The work creates or maintains rural infrastructure, through small scale irrigation and soil conservation projects, re-forestation, and rural road building. EGS projects are designed to be highly intensive in their use of unskilled labor, which typically accounts for over two-thirds of variable costs. Wages are set in the form of piece rates, stipulating rates of pay for a large number of specific tasks, such asdigging, breaking rocks, shifting earth, and transplanting. This paper investigates the effects on the scheme of the dramatic change in the EGS wage schedule in mid-1988. Three issues are addressed: (a) EGS employment, wage rates, and the cost of the scheme to the government after the increase in the statutory minimum wage rate; (b) the determinants of EGS employment and changes incurred by the wage increase; and (c) the availability of local employment at the going EGS wages.Rural Poverty Reduction,Safety Nets and Transfers,Environmental Economics&Policies,Banks&Banking Reform,Services&Transfers to Poor

A Generative Model for Dynamic Networks with Applications

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.Comment: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Data-free Defense of Black Box Models Against Adversarial Attacks

Several companies often safeguard their trained deep models (i.e. details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as black boxes through APIs. Moreover, they may not even provide access to the training data due to proprietary reasons or sensitivity concerns. We make the first attempt to provide adversarial robustness to the black box models in a data-free set up. We construct synthetic data via generative model and train surrogate network using model stealing techniques. To minimize adversarial contamination on perturbed samples, we propose `wavelet noise remover' (WNR) that performs discrete wavelet decomposition on input images and carefully select only a few important coefficients determined by our `wavelet coefficient selection module' (WCSM). To recover the high-frequency content of the image after noise removal via WNR, we further train a `regenerator' network with an objective to retrieve the coefficients such that the reconstructed image yields similar to original predictions on the surrogate model. At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy. Our method improves the adversarial accuracy on CIFAR-10 by 38.98% and 32.01% on state-of-the-art Auto Attack compared to baseline, even when the attacker uses surrogate architecture (Alexnet-half and Alexnet) similar to the black box architecture (Alexnet) with same model stealing strategy as defender. The code is available at https://github.com/vcl-iisc/data-free-black-box-defenseComment: TIFS Submission (Under Review

DAD++: Improved Data-free Test Time Adversarial Defense

With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios. A plethora of works based on adversarial training and regularization-based techniques have been proposed to make these deep networks robust against adversarial attacks. However, these methods require either retraining models or training them from scratch, making them infeasible to defend pre-trained models when access to training data is restricted. To address this problem, we propose a test time Data-free Adversarial Defense (DAD) containing detection and correction frameworks. Moreover, to further improve the efficacy of the correction framework in cases when the detector is under-confident, we propose a soft-detection scheme (dubbed as "DAD++"). We conduct a wide range of experiments and ablations on several datasets and network architectures to show the efficacy of our proposed approach. Furthermore, we demonstrate the applicability of our approach in imparting adversarial defense at test time under data-free (or data-efficient) applications/setups, such as Data-free Knowledge Distillation and Source-free Unsupervised Domain Adaptation, as well as Semi-supervised classification frameworks. We observe that in all the experiments and applications, our DAD++ gives an impressive performance against various adversarial attacks with a minimal drop in clean accuracy. The source code is available at: https://github.com/vcl-iisc/Improved-Data-free-Test-Time-Adversarial-DefenseComment: IJCV Journal (Under Review

Health Infomatics Using Multy-Keyword Rank Search Over Cloud

This projects targets on the productivity of the cloud computing technology in health care industry. Health care sector is one of the largest sectors in the world. Health care industry depends mainly on Information Technology to provide best service and accuracy of information to their patients. System deals with the cloud technology to create network between patients, doctors and health care institution by providing applications services and also by keeping the data in the cloud. System define and solve the challenging problem of privacy preserving multi-keyword search over encrypted cloud data by providing searching through index. Through analysis investigating privacy and efficiency guarantee of proposed schemes is given, and experiments on the real world’s data set further show proposed schemes indeed introduce low overhead on computation and communication. DOI: 10.17762/ijritcc2321-8169.15011

Figure of eight suturing technique with fiber wire for patella fracture: a novel approach

Modified tension band wiring (TBW) is the most commonly used technique for the management of patella fractures. However, all patella fractures are not-amenable to TBW. Tension band wiring, inter-fragmentary screw fixation, and the combination of cerclage wiring and screw fixation are used for internal fixation of these fractures. Surgical treatment is recommended for fractures that either disrupt the extensor mechanism or have greater than 2 to 3 mm of step-off and greater than 1 to 4 mm of displacement. In this series, we present ten cases managed with open reduction and internal fixation with figure of eight suturing technique using fiber wire. This series included ten patients with fractured patella and managed with open reduction and internal fixation with Tension band wiring with fiber wire. Patient demographics, fracture type, time to union, functional outcome, and complications were recorded. Patients were followed up for minimum of 6 months. All fractures went on to unite with average fracture healing time of 13.8 weeks. Mean Lysholm score and Bostman score were 85 and 27 respectively. Nine patients had excellent to good outcomes. One patient had poor outcome because of knee stiffness. Open reduction and internal fixation with figure of eight suturing technique using fiber wire are an efficient method for the management of severely comminuted and multi-fragmentary patella fractures with minimum complications

Preparation and Evaluation of Sodium Alginate Microparticles using Pepsin

Aim: The main aim of this article is to prepare and evaluate sodium alginate microparticles and evaluate on the basis of their characterization. The drug is dissolved, encapsulated or attached to a microparticles matrix. Depending upon method of preparation microparticles were obtained. Microparticles were developed as a carrier for vaccines and other disease like rheumatoid arthritis, cancer etc. Microparticles were developed to increase the efficacy of active pharmaceutical ingredient to a specific targeted site. Material and Method: Microparticles of Sodium Alginate, Pepsin and Calcium Chloride were prepared in six batches (A-F) with different ratio of sodium alginate and calcium chloride respectively i.e. (0.25:2.5), (0.25:5), (0.25:7.5), (0.5:2.5), (0.5:5), (0.5:7.5) by using a homogenizing method. Microparticles were evaluated for particle size distribution, zeta potential and morphology. Result and Discussion:  The normal particle size of each of the six batches were analyzed by Zeta Sizer (Delsa C Particle Analyzer) and it was found that the Batch B (0.25:5) delivered the best microparticles with size distribution of 1.2731 (µm). All batches were seen under Motic magnifying microscope by using the Sulforhodamine B (M.W. 479.02) color as staining dye. Microparticles was found to be semi spherical in shape. Conclusion: Results of all the six batches was contrasted based on particle size investigation, zeta potential and morphology. Batch B (0.25:5) was considered as the best formulation. Key words:  Micro Particle, Pepsin, Sodium Alginate and Calcium Chloride, Sulforhodamine B, Zeta Sizer