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The vulnerability of deep networks towards adversarial perturbations has motivated the researchers to design detection and mitigation algorithms. Inspired by the dropout and dropconnect algorithms as well as augmentation techniques, this paper presents “PixelMask” based data augmentation as an efficient method of reducing the sensitivity of convolutional neural networks (CNNs) towards adversarial attacks. In the proposed approach, samples generated using PixelMask are used as augmented data, which helps in learning robust CNN models. Experiments performed with multiple databases and architectures show that the proposed PixelMask based data augmentation approach improves the classification performance on adversarially perturbed images. The proposed defense mechanism can be applied effectively for different adversarial attacks and can easily be combined with any deep neural network (DNN) architecture to increase the robustness. The effectiveness of the proposed defense is demonstrated in gray-box, white-box, and unseen train-test attack scenarios. For example, on the CIFAR-10 database under adaptive attack (i.e., projected gradient descent), the proposed PixelMask is able to improve the recognition performance of CNN by at-least 22.69%. Another advantage of the proposed algorithm over several existing defense algorithms is that the proposed defense either is able to retain or increase the classification accuracy of clean examples.

The present study was undertaken to investigate the effect of dietary replacement of commonly used vegetable oil (sunflower oil, SFO) with n-3 polyunsaturated fatty acids (PUFA) rich oil sources on broiler chicken performance, carcass yield, meat fatty acid composition, keeping quality and sensory attributes of meat. In the current experiment, 300 day-old Krishibro broiler chicks were randomly distributed to 5 dietary groups (50 replicates with 6 chicks in each) prepared by replacing SFO (2% and 3% of diet during starter and finisher periods, respectively) with n-3 PUFA rich soybean oil (SO), mustard oil (MO), linseed oil (LO) or fish oil (FO) on weight basis. Variation in oil sources had no influence (P > 0.05) on performance and carcass yield. Supplementation of MO, LO or FO significantly (P

Cytomegalovirus (CMV) reactivation may lead to CMV disease associated with high morbidity and mortality in patients after hematopoietic stem cell transplantation (HSCT); the identification of clinically relevant markers may aid in the identification of patients at increased risk for developing CMV-associated complications. We evaluated the phosphorylation of signal transducer and activator of transcription 5 (STAT5) in CD4+ T cells, CD8+ T cells, and TCRγδ T cells in response to stimulation with IL-7 or IL-2 after HSCT by analyzing blood samples taken monthly 1 to 6 months after HSCT. Patients were monitored weekly with a quantitative PCR from the time of engraftment for CMV viral load in whole blood until at least day 100 after HSCT. We identified a correlation between clinical outcome regarding CMV replication and the ability to respond to IL-7 and IL-2 defined by STAT5 phosphorylation (pSTAT5). Patients with recurrent or prolonged CMV replications had significantly lower pSTAT5 upon stimulation of T cells with either IL-7 or IL-2 at time points 1 through 3 than those without CMV replication (P < .05). This was also found after stimulation of CD8+ T cells at time point 2 (P < .05). We conclude that reduced responses to IL-7, reflected by pSTAT5, may represent a clinically relevant functional biomarker for individuals at increased risk for CMV reactivation; our data may also aid in designing better strategies to improve anti-CMV immune responses without increasing the risk of developing graft-versus-host disease.

A biometric system is an instance of a pattern recognition system with well-defined error conditions, which allows objective statistical error analysis. Biometrics is an emerging technology with fierce competition between many manufacturers using a variety of biometrics, such as, fingerprints, for recognizing human identities. Manufacturers of biometric systems are continuously refining the technology and claiming "high accuracy." This may be defined simply as a system that works or as a system that makes very few or no errors. Obviously, such loose definitions of accuracy are undesirable and there is a need for a precise definition. We argue that biometric match score accuracy is best expressed in terms of a curve, the Receiver Operating Characteristic curve. More importantly, we argue that confidence intervals, or margins of error, should be provided for this curve. This allows for determining whether accuracy differences between systems are really statistically significant. We introduce a novel bootstrap technique for computing the confidence regions of the error estimates and compare them to a commonly used parametric method. This bootstrap technique is inspired by the moving blocks bootstrap, which samples with replacement from blocks of data thereby accounting for dependence among the data. Our approach samples with replacement from specifically determined subsets of the data. We call this new bootstrap technique "the subsets bootstrap."

Currently there is much interest in the use of biometrics for authentication and identification applications. This has been heightened most recently because of the threat of terrorism. Biometrics authentication and identification systems offer several advantages over systems based on knowledge or possession such as unsupervised (legacy) password/PIN-based systems and supervised (legacy) passport-based systems. To optimize security it is important that biometrics authentication systems are designed to withstand different sources of attack. We identify some such threats to biometrics systems and detail issues related to the tradeoff between security and convenience. We further show how to estimate a biometrics' intrinsic security, sometimes called a biometrics' individuality, with fingerprints as an example.

Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact.

This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (“Gabor features”) and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image—it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query.

A reliable method for extracting structural features from fingerprint images is presented. Viewing fingerprint images as a textured image, an orientation flow field is computed. The rest of the stages in the algorithm use the flow field to design adaptive filters for the input image. To accurately locate ridges, a waveform projection-based ridge segmentation algorithm is used. The ridge skeleton image is obtained and smoothed using morphological operators to detect the features. A large number of spurious features from the detected set of minutiae is deleted by a postprocessing stage. The performance of the proposed algorithm has been evaluated by computing a “goodness index” (GI) which compares the results of automatic extraction with manually extracted ground truth. The significance of the observed GI values is determined by comparing the index for a set of fingerprints against the GI values obtained under a baseline distribution. The detected features are observed to be reliable and accurate.

The role of different functional groups in humic acid was investigated for complex formation during the interaction of Fe+2 and Fe+3 ions with humic acid, oxidized humic acid, acetylated humic acid and decarboxylated humic acid. The formation of two types of complexes, ionic and non-ionic, was observed. The results indicate that the proton releasing groups, e.g. carboxyl and phenolic hydroxyl groups, are responsible for the formation of a ionic complex, and the formation of a non-ionic complex involves the carboxyl and carbonyl (keto) groups. The percentage of the ionic iron in the complex increases and that of the non-ionic iron decreases with increase of pH.

In spite of the recent development of many rapid methods of diagnosing viral and mycoplasmal disease, serology still remains an important diagnostic tool, being the only method readily available in many laboratories. The serological response to most viral and mycoplasmal infections is such that at least a few days must elapse before it can be detected even by the most sensitive tests presently available. The complement fixation test, though one of the oldest and least sensitive serological tests developed, is still widely used. Hacmagglutination inhibition tests are applicable to infections caused by the haemagglutinating viruses. Passive agglutination of antigen coated particles can be used for the non-haemagglutinaling agents. More recently techniques using labelled immunological reactants such as radio-immunoassays, ELISA and fluorescent antibody assays have been used. These techniques, because of their adaptability to automation, will probably replace the older methods. The persistence of antibodies developed during infections in the remote past and anamnestic responses necessitate the collection of paired specimens to make any useful interpretation of results. This tends to discourage clinicians from using serological tests for diagnosis. However, the development of techniques to detect specific antibody in the IgM fraction of sera has made possible the diagnosis of recent infections with a single specimen of serum.