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Background: Altered patterns of amino acid profiles are observed in various pathological conditions including nutrition related disorders, cancer, diabetes, urea cycle defects, mitochondrial respiratory chain disorders, and aminoacidopathies. Aim and Objectives: To develop a cost effective Reverse Phase-High Performance Liquid Chromatography (RP-HPLC) method for the quantification of free amino acids in neonatal Dried Blood Spot (DBS) samples. Material and Methods: Free amino acids were extracted from neonatal DBS through elution with ice-cold methanol/2-mercaptoethanol. The extracted samples were then derivitized by using O-Pthalaldehyde (OPA)/2-mercaptoethanol, which resulted in the formation of OPA derivatives of amino acids. These derivatives were quantified by RP-HPLC. The developed method was validated according to the International Conference on Harmonization ICH Q2 (R1) Guidelines and applied to clinically confirmed aminoacidopathies. Results: A good linearity was observed from 2.5 to 1000 μM. The between run imprecision (on two different days) though varied over a wide range for different amino-acids, the Coefficience of Variation (CV) was found to be less than 5% (range; 0.1 to 5% for different amino acids) and the recovery was found to be around 82-125% (for spiked amino acids). The quantified concentration of amino acids in plasma and DBS sample were found to be almost similar except for few amino acids like glycine, tyrosine, alanine, methionine and lysine which showed higher values in dried blood spot samples. DBS amino acid concentrations determined by RP-HPLC showed concordance to the concentrations obtained through Tandem Mass Spectrometry (TMS) of DBS/plasma. Reference intervals of amino acids were established in term neonates. The proposed method was applied to the analysis of samples obtained from confirmed aminoacidopathies. Conclusion: The present approach with DBS offers a valid alternative to the plasma/serum aminoacid profiling in HPLC or DBS in Tandem mass method, with advantages like lower sample volume, feasibility, affordability, improved specificity and can be attempted in a simple diagnostic setup with acceptable accuracy and precision.

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 articles in this special section provide a glimpse of the diverse research challenges in adopting blockchain technology into mainstream applications. The four articles focus on the following core issues: scalability, transparency versus privacy, standardization, ecosystem, and integration.

A reliable LULC model aims at predicting the spatial distribution of specific LULC classes for a later year by utilizing the trends from previous years thereby helps in appropriate LULC planning. The Ganga River Basin (GRB) has undergone significant LULC changes during past decades. The changes in LULC pattern was investigated for 1975 and 2010 to have better understanding of the conversion process and thereby predicting the future trend for 2045 using Dyna-CLUE (Conversion of Land-Use and its Effects) model. Four types of data were fed into the model i.e., (a) spatial policies and restrictions; (b) LULC type specific conversion settings; (c) LULC requirements (demands) and (d) location characteristics. The possible and impossible conversions among LULC classes were dealt with through Restriction and No Restriction area. The land conversion allocation was determined by establishing preference between LULC classes and the driving factors using binary logistic regression. Relative Operating Characteristic curves provided an overall value of 0.86 implying acceptability of regression results. The simulated result (with ‘no restriction’ area criteria) showed 578,296 km2 agriculture area in 2010 and 579,235 km2 for 2045, wherein 575,874 km2 (99.58%) of agriculture area could remain unchanged during 2010–2045; while with the restricted area, agriculture area of 577,675 km2 in 2010 and 578,516 km2 for 2045; whereas 576,242 km2 (99.75%) of agriculture area may remain unchanged during 2010–2045. Biophysical drivers namely altitude, slope, aspect, soil types, precipitation and temperature, emerged as major controlling factors for LULC change in GRB. Logistic regression analysis showed that population density is positively related with agriculture and expansion of settlements.

Introduction: The Government of India has formulated the Cigarettes and Other Tobacco Products Act (COTPA) to implement measures to ensure that effective protection is provided to nonsmokers from involuntary exposure to tobacco smoke. Bhubaneswar is the capital city of Odisha, India, was declared as “Tobacco Smoke Free City” in 2010. For strengthening the implementation of the COTPA Act, an effective regular assessment is needed, and hence, an observational study was planned to assess the current violations of Tobacco Smoking in Bhubaneswar. Materials and Methods: In this observational study, 416 different places were chosen from four different zones of Bhubaneswar. Data were collected with the help of Mobile enabled global positioning system (GPS) technology and pretested structured questionnaire. Results: In this study, 52.88% places were found to be having smoking violations. The study shows maximum violations have occurred in public places (90.89%) followed by transit places (75%). Violations were found to be very low in government buildings, educational, medical institutes, and hotel/restaurants. The average number of smokers in the city was found to be 4.90/place and the average number of smokers was found to be 4.37/public place. Conclusion: In this study, GPS-enabled Mobile App can be used to identify the different locations, where a violation of law occurs. This may help administrators to properly plan and implement the law. Even though Bhubaneswar was declared “Tobacco Smoke free city” in 2010, it is still lacking behind in fulfilling the implementation of law, to reduce Second Hand Smoking.

Deep learning models such as Convolutional Neural Networks (CNNs) have shown the potential to classify medical images for accurate diagnosis. These techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed as a service on a cloud platform. Fully Homomorphic Encryption (FHE) can enable CNN inference on encrypted data and help mitigate such concerns. However, encrypted CNN inference faces the fundamental challenge of optimizing the computations to achieve an acceptable trade-off between accuracy and practical computational feasibility. Current approaches for encrypted CNN inference demonstrate feasibility typically on smaller images (e.g., MNIST and CIFAR-10 datasets) and shallow neural networks. This work is the first to show encrypted inference results on a real-world dataset for melanoma detection with large-sized images of skin lesions based on the Cheon-Kim-Kim-Song (CKKS) encryption scheme available in the open-source HElib library. The practical challenges related to encrypted inference are first analyzed and inference experiments are conducted on encrypted MNIST images to evaluate different optimization strategies and their role in determining the throughput and latency of the inference process. Using these insights, a modified LeNet-like architecture is designed and implemented to achieve the end goal of enabling encrypted inference on melanoma dataset. The results demonstrate that 80% classification accuracy can be achieved on encrypted skin lesion images (security of 106 bits) with a latency of 51 seconds for single image inference and a throughput of 18,000 images per hour for batched inference, which shows that privacy-preserving machine learning as a service (MLaaS) based on encrypted data is indeed practically feasible.

Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, the current face recognition systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition. Most of the existing disguise datasets contain images with limited variations, often captured in controlled settings. This does not simulate a real world scenario, where both intentional and unintentional unconstrained disguises are encountered by a face recognition system. In this paper, a novel Disguised Faces in the Wild (DFW) dataset is proposed which contains over 11000 images of 1000 identities with different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings. This is the first-of-a-kind dataset with the availability of impersonator and genuine obfuscated face images for each subject. The proposed dataset has been analyzed in terms of three levels of difficulty: (i) easy, (ii) medium, and (iii) hard in order to showcase the challenging nature of the problem. It is our view that the research community can greatly benefit from the DFW dataset in terms of developing algorithms robust to such adversaries. The proposed dataset was released as part of the First International Workshop and Competition on Disguised Faces in the Wild at CVPR, 2018. This paper presents the DFW dataset in detail, including the evaluation protocols, baseline results, performance analysis of the submissions received as part of the competition, and three levels of difficulties of the DFW challenge dataset.

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks, (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, and three publicly available face databases demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. We also evaluate the proposed approaches on four existing quasi-imperceptible distortions: DeepFool, Universal adversarial perturbations, $$l_2$$ , and Elastic-Net (EAD). The proposed method is able to detect both types of attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.

Adversarial perturbations have demonstrated the vulnerabilities of deep learning algorithms to adversarial attacks. Existing adversary detection algorithms attempt to detect the singularities; however, they are in general, loss-function, database, or model dependent. To mitigate this limitation, we propose DAMAD--a generalized perturbation detection algorithm which is agnostic to model architecture, training data set, and loss function used during training. The proposed adversarial perturbation detection algorithm is based on the fusion of autoencoder embedding and statistical texture features extracted from convolutional neural networks. The performance of DAMAD is evaluated on the challenging scenarios of cross-database, cross-attack, and cross-architecture training and testing along with traditional evaluation of testing on the same database with known attack and model. Comparison with state-of-the-art perturbation detection algorithms showcase the effectiveness of the proposed algorithm on six databases: ImageNet, CIFAR-10, Multi-PIE, MEDS, point and shoot challenge (PaSC), and MNIST. Performance evaluation with nearly a quarter of a million adversarial and original images and comparison with recent algorithms show the effectiveness of the proposed algorithm.

Modern AI systems are reaping the advantage of novel learning methods. With their increasing usage, we are realizing the limitations and shortfalls of these systems. Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, high opacity in terms of revealing the lineage of the system, how they were trained and tested, and under which parameters and conditions they can reliably guarantee a certain level of performance, are some of the most prominent limitations. Ensuring the privacy and security of the data, assigning appropriate credits to data sources, and delivering decent outputs are also required features of an AI system. We propose the tutorial on “Trustworthy AI” to address six critical issues in enhancing user and public trust in AI systems, namely: (i) bias and fairness, (ii) explainability, (iii) robust mitigation of adversarial attacks, (iv) improved privacy and security in model building, (v) being decent, and (vi) model attribution, including the right level of credit assignment to the data sources, model architectures, and transparency in lineage.

Blockchain has emerged as a leading technology that ensures security in a distributed framework. Recently, it has been shown that blockchain can be used to convert traditional blocks of any deep learning models into secure systems. In this research, we model a trained biometric recognition system in an architecture which leverages the blockchain technology to provide fault tolerant access in a distributed environment. The advantage of the proposed approach is that tampering in one particular component alerts the whole system and helps in easy identification of `any' possible alteration. Experimentally, with different biometric modalities, we have shown that the proposed approach provides security to both deep learning model and the biometric template., Published in IEEE BTAS 2019

In many areas in machine learning, decision trees play a crucial role in classification and regression. When a decision tree based classifier is hosted as a service in a critical application with the need for privacy protection of the service as well as the user data, fully homomorphic encrypted can be employed. However, a decision node in a decision tree can’t be directly implemented in FHE. In this paper, we describe an end-to-end approach to support privacyenhanced decision tree classification using IBM supported open-source library HELib. Using several options for building a decision node and employing oblivious computations coupled with an argmax function in FHE we show that a highly secure and trusted decision tree service can be enabled.

Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L 2 , Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.

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

While significant advancements have been made in the field of multimedia forensics to detect altered content, existing techniques mostly focus on enabling the content recipient to verify the content integrity without any inputs from the content creator. In many application scenarios, the creator has a strong incentive to establish the provenance and integrity of the multimedia data created and released by him. Hence, there is a strong need for mechanisms that allow the content creator to prove the authenticity of the released content. Since blockchain technology provides an immutable distributed database, it is an ideal solution for reliably time-stamping content with its creation time and storing an irrefutable signature of the content at the time of its creation. However, a simple digital signature scheme does not allow modification of the content after the initial commitment. Authorized multimedia content alteration by its creator is often necessary (e.g., redaction of faces to protect the privacy of individuals in a video, redaction of sensitive fields in a text document) before the content is distributed. The main contributions of this paper are: (i) a novel sanitizable signature scheme that enables the content creator to prove the integrity of the redacted content, while preventing the recipients from reconstructing the redacted segments based on the published commitment, and (ii) a blockchain-based solution for securely managing the sanitizable signature. The proposed solution employs a robust hashing scheme using chameleon hash function and Merkle tree to generate the initial signature, which is stored on the blockchain. The auxiliary data required for the integrity verification step is retained by the content creator and only a signature of this auxiliary data is stored on the blockchain. Any modifications to the multimedia content requires only updating the signature of the auxiliary data, which is securely recorded on the blockchain. We demonstrate that the proposed approach enables verification of integrity of redacted multimedia content without compromising the content privacy requirements.

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash as well as symmetric/asymmetric encryption and decryption algorithms ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. We provide initial evidence that skin type alone is not the driver for this disparity by conducting novel stability experiments that vary an image's skin type via color-theoretic methods, namely luminance mode-shift and optimal transport. We evaluate the effect of skin type change on the gender classification decision of a pair of state-of-the-art commercial and open-source gender classifiers. The results raise the possibility that broader differences in ethnicity, as opposed to the skin type alone, are what contribute to unequal gender classification accuracy in face images.

While deep learning is a valuable tool for solving many tough problems in computer vision, the success of deep learning models is typically determined by: (i) availability of sufficient training data, (ii) access to extensive computational resources, and (iii) expertise in selecting the right model and hyperparameters for the selected task. Often, the availability of data is the hard part due to compliance, legal, and privacy constraints. Cryptographic techniques such as fully homomorphic encryption (FHE) offer a potential solution by enabling processing on encrypted data. While prior work has been done on using FHE for inferencing, training a deep neural network in the encrypted domain is an extremely challenging task due to the computational complexity of the operations involved. In this paper, we evaluate the feasibility of training neural networks on encrypted data in a completely non-interactive way. Our proposed system uses the open-source FHE toolkit HElib to implement a Stochastic Gradient Descent (SGD)-based training of a neural network. We show that encrypted training can be made more computationally efficient by (i) simplifying the network with minimal degradation of accuracy, (ii) choosing appropriate data representation and resolution, and (iii) packing the data elements within the ciphertext in a smart way so as to minimize the number of operations and facilitate parallelization of FHE computations. Based on the above optimizations, we demonstrate that it is possible to achieve more than 50x speed up while training a fully-connected neural network on the MNIST dataset while achieving reasonable accuracy (96%). Though the cost of training a complex deep learning model from scratch on encrypted data is still very high, this work establishes a solid baseline and paves the way for relatively simpler tasks such as fine-tuning of deep learning models based on encrypted data to be implemented in the near future.

Interest in Management Intensive Grazing (MIG) [a situation where grazing animals are moved to a fresh pasture every few days in order to have access to adequate forage] practices by farmers have increased steadily over the years. Many research publications on grazing advocate the financial and environmental benefits of grazing. Understanding the challenges of MIG can be an important piece of information for a dairy farmer. A survey was conducted to determine how farmers in the southeastern region perceive the barriers to the adoption of MIG. A greater percentage of MIG southeastern farmers were satisfied or very satisfied with their farm profit level compared to other practices. However, the amount of work to start pasture management, and the lack of on-farm technical assistance were barriers for many MIG operations.

Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in an unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.

Background: Hemophilia-an X linked congenital bleeding disorder because of deficiency in the coagulation factor VIII (in hemophilia A) or factor IX (in hemophilia B). The deficiency is due to the result of mutations of the clotting factor genes. Acute and chronic pain is commonly seen pain with hemophilia patients. Assessing of the cause of pain is essential for proper management. The choice of appropriate pain-relieving measures is difficult and challenging, as there are many complex factors affecting pain perception. There may be differences in patient’s experiences and their response to pain; hence, an individualized approach to pain management is required. Aim and Objectives: The objectives of the study were (i) to assess the efficacy of Paracetamol and Tramadol in pain management among hemophilic patient and (ii) to assess the adverse drug monitoring of Paracetamol and Tramadol among hemophilic patient. Materials and Methods: This study was a hospital-based prospective and observational crossover study, conducted at Hassan institute of Medical Sciences, Hassan, A 800 bedded multispecialty tertiary care teaching hospital over a period of 6 months (March 2019-August 2019). A prospective, observational, and crossover study, conducted on diagnosed and confirmed cases of hemophilia total 18 patients, age ranging from 18 to 50 years and being treated with Paracetamol 500 mg TID for 5 days followed washout period of 7 days, then Tramadol 50 mg BID for 5 days. Pain assessment (Visual analog scale) is done before (baseline) and after 5 days of each drugs. The results were analyzed using descriptive statistics. Results: In our study, out of 18 male patients who are enrolled in the study from inpatient medicine department, patients belonged to age group of 18–50 years. Statistical Significant decrease in pain was seen with Tramadol compared to Paracetamol. Conclusion: Tramadol 50 mg BD is better than Paracetamol 500mg TID for pain management in hemophilia patients. As all patients would be suffering from deep seated pain, which is resolved by weak Opiod. Safer alternative for mild pain relief is Paracetamol (11%). Life expectancy and Quality of life will be improved with appropriate pain relief medication.

The mechanisms whereby immune therapies affect progression of type 1 diabetes (T1D) are not well understood. Teplizumab, an FcR nonbinding anti-CD3 mAb, has shown efficacy in multiple randomized clinical trials. We previously reported an increase in the frequency of circulating CD8(+) central memory (CD8CM) T cells in clinical responders, but the generalizability of this finding and the molecular effects of teplizumab on these T cells have not been evaluated. We analyzed data from two randomized clinical studies of teplizumab in patients with new- and recent-onset T1D. At the conclusion of therapy, clinical responders showed a significant reduction in circulating CD4(+) effector memory T cells. Afterward, there was an increase in the frequency and absolute number of CD8CM T cells. In vitro, teplizumab expanded CD8CM T cells by proliferation and conversion of non-CM T cells. Nanostring analysis of gene expression of CD8CM T cells from responders and nonresponders versus placebo-treated control subjects identified decreases in expression of genes associated with immune activation and increases in expression of genes associated with T-cell differentiation and regulation. We conclude that CD8CM T cells with decreased activation and regulatory gene expression are associated with clinical responses to teplizumab in patients with T1D.

FOXP3+ Tregs are central for the maintenance of self-tolerance and can be defective in autoimmunity. In multiple sclerosis and type-1 diabetes, dysfunctional self-tolerance is partially mediated by a population of IFNγ-secreting Tregs. It was previously reported that increased NaCl concentrations promote the induction of proinflammatory Th17 cells and that high-salt diets exacerbate experimental models of autoimmunity. Here, we have shown that increasing NaCl, either in vitro or in murine models via diet, markedly impairs Treg function. NaCl increased IFNγ secretion in Tregs, and reducing IFNγ — either by neutralization with anti-IFNγ antibodies or shRNA-mediated knockdown — restored suppressive activity in Tregs. The heightened IFNγ secretion and loss of Treg function were mediated by the serum/glucocorticoid-regulated kinase (SGK1). A high-salt diet also impaired human Treg function and was associated with the induction of IFNγ-secreting Tregs in a xenogeneic graft-versus-host disease model and in adoptive transfer models of experimental colitis. Our results demonstrate a putative role for an environmental factor that promotes autoimmunity by inducing proinflammatory responses in CD4 effector cells and Treg pathways.

Deep learning algorithms provide state-of-the-art results on a multitude of applications. However, it is also well established that they are highly vulnerable to adversarial perturbations. It is often believed that the solution to this vulnerability of deep learning systems must come from deep networks only. Contrary to this common understanding, in this article, we propose a non-deep learning approach that searches over a set of well-known image transforms such as Discrete Wavelet Transform and Discrete Sine Transform, and classifying the features with a support vector machine-based classifier. Existing deep networks-based defense have been proven ineffective against sophisticated adversaries, whereas image transformation-based solution makes a strong defense because of the non-differential nature, multiscale, and orientation filtering. The proposed approach, which combines the outputs of two transforms, efficiently generalizes across databases as well as different unseen attacks and combinations of both (i.e., cross-database and unseen noise generation CNN model). The proposed algorithm is evaluated on large scale databases, including object database (validation set of ImageNet) and face recognition (MBGC) database. The proposed detection algorithm yields at-least 84.2% and 80.1% detection accuracy under seen and unseen database test settings, respectively. Besides, we also show how the impact of the adversarial perturbation can be neutralized using a wavelet decomposition-based filtering method of denoising. The mitigation results with different perturbation methods on several image databases demonstrate the effectiveness of the proposed method.

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.

The microbiome affects development and activity of the immune system, and may modulate immune therapies, but there is little direct information about this control in vivo. We studied how the microbiome affects regulation of human immune cells in humanized mice. When humanized mice were treated with a cocktail of 4 antibiotics, there was an increase in the frequency of effector T cells in the gut wall, circulating levels of IFN-γ, and appearance of anti-nuclear antibodies. Teplizumab, a non–FcR-binding anti-CD3ε antibody, no longer delayed xenograft rejection. An increase in CD8+ central memory cells and IL-10, markers of efficacy of teplizumab, were not induced. IL-10 levels were only decreased when the mice were treated with all 4 but not individual antibiotics. Antibiotic treatment affected CD11b+CD11c+ cells, which produced less IL-10 and IL-27, and showed increased expression of CD86 and activation of T cells when cocultured with T cells and teplizumab. Soluble products in the pellets appeared to be responsible for the reduced IL-27 expression in DCs. Similar changes in IL-10 induction were seen when human peripheral blood mononuclear cells were cultured with human stool samples. We conclude that changes in the microbiome may impact the efficacy of immunosuppressive medications by altering immune regulatory pathways.

Increased individual mobility has pushed the modern society needs for a reliable individual identity verification system as a critical component in many transactions in commercial industries, public sectors and government domains. The requirement for an ideal human identity verification is critical to security and prevention of identity fraud. Thus, trusted identity management has become an essential part of contemporary system infrastructure. It is now well accepted that biometrics-the science of identifying a person (or verifying their identity) based on their physiological or behavioral characteristics-can provide significant value when building such systems. Three key cornerstones in a trusted biometrics-based identity system include the following: (a) A trusted identity enrollment process (b) A trusted identity verification process (c) An identity credential management mechanism. In this chapter, we present several emerging developments in mobile biometrics technologies with particular focus on futuristic cognitive authentication systems for enabling large-scale trusted identity management systems based on biometrics and also biometrics identity services in the cloud. Biometrics is excellent mechanisms for the authentication or identification of individuals because of the credential's uniqueness and persistence almost over the lifetime of the person. Biometric identity services in the cloud enable mobile biometrics wide adoption economically and also take advantages of adjacent technology advancements.

Immune-deficient mice, reconstituted with human stem cells, have been used to analyze human immune responses in vivo. Although they have been used to study immune responses to xenografts, allografts, and pathogens, there have not been models of autoimmune disease in which the mechanisms of the pathologic process can be analyzed. We have found that reconstituted “humanized” mice treated with anti–CTLA-4 Ab (ipilimumab) develop autoimmune disease characterized by hepatitis, adrenalitis, sialitis, anti-nuclear Abs, and weight loss. Induction of autoimmunity involved activation of T cells and cytokine production, and increased infiltration of APCs. When anti–CTLA-4 mAb–treated mice were cotreated with anti-CD3 mAb (teplizumab), hepatitis and anti-nuclear Abs were no longer seen and weight loss did not occur. The anti-CD3 blocked proliferation and activation of T cells, release of IFN-γ and TNF, macrophage infiltration, and release of IP-10 that was induced with anti–CTLA-4 mAb. We also found increased levels of T regulatory cells (CD25+CD127−) in the spleen and mesenteric lymph nodes in the mice treated with both Abs and greater constitutive phosphorylation of STAT5 in T regulatory cells in spleen cells compared with mice treated with anti–CTLA-4 mAb alone. We describe a model of human autoimmune disease in vivo. Humanized mice may be useful for understanding the mechanisms of biologics that are used in patients. Hepatitis, lymphadenopathy, and other inflammatory sequelae are adverse effects of ipilimumab treatment in humans, and this study may provide insights into this pathogenesis and the effects of immunologics on autoimmunity.

Type 1 diabetes is an organ-specific autoimmune disease, characterized by selective destruction of insulin-producing pancreatic β-cells by T-cell-mediated inflammation. Beginning with studies of cyclosporin A in the 1980s, but with more activity in the past decade, there have been a number of clinical trials to test whether immunotherapies can arrest the decline in C-peptide, which is associated with progression of type 1 diabetes leading to the metabolic instability that characterizes the disease. One of the most promising agents, teplizumab , is an FcR-nonbinding anti-CD3 monoclonal antibody that has been tested in Phase II - III clinical trials and was shown to preserve the C-peptide levels and reduce the need for exogenous insulin.In this review, we discuss the recent update on clinical data obtained from trials of teplizumab in type 1 diabetes, the drug's postulated mechanism of action and the identification of responders to therapy. We highlight the results of recent trials as well as the lessons that have been learned from the clinical trials involving selection of end points and the inclusion of diverse study populations.Teplizumab has been shown to preserve β cell function in patients; however, it does not represent a 'cure' for patients, and its efficacy does entail a significant advance in arresting the progression of the disease toward complete insulin deficiency and reliance on exogenous insulin.

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.

Classifier fusion is a well-studied problem in which decisions from multiple classifiers are combined at the score, rank, or decision level to obtain better results than a single classifier. Subsequently, various techniques for combining classifiers at each of these levels have been proposed in the literature. Many popular methods entail scaling and normalizing the scores obtained by each classifier to a common numerical range before combining the normalized scores using the sum rule or another classifier. In this research, we explore an alternative method to combine classifiers at the score level. The Pool Adjacent Violators (PAV) algorithm has traditionally been utilized to convert classifier match scores to confidence values that model posterior probabilities for test data. The PAV algorithm and other score normalization techniques have studied the same problem without being aware of each other. In this first ever study to combine the two, we propose the PAV algorithm for classifier fusion on publicly available NIST multi-modal biometrics score dataset. We observe that it provides several advantages over existing techniques and find that the interpretation learned by the PAV algorithm is more robust than the scaling learned by other popular normalization algorithms such as min-max. Moreover, the PAV algorithm enables the combined score to be interpreted as confidence and is able to further improve the results obtained by other approaches. We also observe that utilizing traditional normalization techniques first for individual classifiers and then normalizing the fused score using PAV offers a performance boost compared to only using the PAV algorithm.

Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively.

Often deep learning methods are associated with huge amounts of training data. The deeper the network gets, the larger is the need for training data. A large amount of labeled data helps the network learn about the variations it needs to handle in the prediction stage. It is not easy for everyone to get access to huge amounts of labeled data leaving a few to have the luxury to design very deep networks. In this paper, we propose to flatten the disparity by using the modeling methods to minimize the need for huge amounts of data for training a deep network. Using face recognition as an example, we demonstrate how limited labeled data can be leveraged to obtain near state of the art performance with generalization capability across multiple databases. In addition, we show that the normalization in the overall network can improve the speed and resource requirement for the prediction/inferencing stage.

There is increasing interest in the role of infection in triggering autoimmune diseases (1, 2). With infection, a pro-inflammatory response is induced to protect the host which includes the activation and expansion of innate and adaptive immune cells. However, this pro-inflammatory response must be properly down-regulated once the pathogen is controlled or eliminated to maintain tolerance and limit tissue pathology. In some individuals, these regulatory mechanisms do not work optimally, leading to pathogenic autoimmunity. Therefore, identifying quantitative and qualitative differences in immune cells between patients who can properly down-regulate their immune response after infection from those who cannot is critical to our understanding of infection-induced autoimmunity. Lyme arthritis, a late stage manifestation of infection with the tick-borne spirochete Borrelia burgdorferi (Bb) (3), provides a human model of infection that may lead to these two alternative outcomes (4). Most patients can be treated successfully with antibiotics, called antibiotic-responsive arthritis (5, 6). However, in a small percentage of patients, proliferative synovitis persists for months or years after ≥3 months of oral and IV antibiotics, called antibiotic-refractory arthritis (7). This outcome is postulated to result from persistent infection, retained spirochetal antigens, infection-induced autoimmunity, or a combination of these factors. In animal models, a small number of attenuated spirochetes may survive despite 1 month of antibiotic therapy (8), but in patients with antibiotic-refractory arthritis, culture and PCR results for Bb in synovial tissue have been uniformly negative after ≥3 months of antibiotics (9). Additionally, in MyD88−/− mice, which have a high pathogen load, spirochetal antigens are retained near cartilage surfaces after antibiotic therapy (10), but the relevance of this finding to human antibiotic-refractory arthritis is not yet clear. In the human disease, data supports the infection-induced autoimmunity model (7, 11, 12). For example, antibiotic-refractory arthritis is associated with specific HLA-DR alleles (particularly DRB1*0101 and 0401) (11), a risk factor commonly associated with autoimmune diseases. We postulate that these patients are unable to properly down-regulate their immune response with antibiotic therapy and apparent spirochetal killing leading to immune dysregulation and antibiotic-refractory arthritis. Previously, we showed that in patients with antibiotic-refractory arthritis, the percentage of CD4+FOXP3+ Treg cells in SF correlated inversely with the post-antibiotic duration of arthritis (13), implying that lower numbers of Treg led to slower arthritis resolution. Furthermore, suppression assays using cells from 2 patients with refractory arthritis showed that CD25-positive T cells (Treg) from PB and SF suppressed the proliferation of CD25-negative T cells (Teff) at a 1-to-1 ratio equally well, but CD25-negative T cells (Teff) from SF were more resistant to suppression than from PB. However, in this study, the expression of FOXP3 within various CD4+CD25 T cell subpopulations, the expression of activating or inhibitory T cell co-receptors, and the ability of these patients’ Treg cells to suppress cytokine secretion were not determined. In our current study, we compared the frequency, phenotype and function of immune cells in PB and SF from patients with antibiotic-responsive or antibiotic-refractory Lyme arthritis. Critical differences between the 2 patient groups were found in the CD4+CD25hi+ T cell population in SF. This cell population in the refractory group often had lower frequencies of Treg, higher expression of activation co-receptors, and less effective inhibition of pro-inflammatory responses, leading to immune dysregulation and persistent synovitis.

A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification. Results on retrievals from the Internet using popular celebrities show the efficacy of our approach after we cleanse the images collection retrieved and applying our algorithm to the collection.

We aimed to compare white matter structural changes in specific tracts by diffusion tensor imaging (DTI) tractography in patients with bipolar disorder (BD) I, non-ill first-degree relatives (FDR) of the patients, and healthy controls (HC).In a cross-sectional study, we studied right-handed subjects consisting of 16 euthymic BD I patients, 15 FDR, and 15 HC. The anterior thalamic radiation, uncinate fasciculus, corpus callosum, and cingulum bundle were reconstructed by DTI tractography. Mean fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were compared for group differences followed by post-hoc analysis.The three groups did not differ in terms of sociodemographic variables. There were significant group differences in the FA values among the BD I patients, their FDR, and the HC for the corpus callosum, the dorsal part of the right cingulum bundle, the hippocampal part of the cingulum bundle bilaterally, and the uncinate fasciculus (P0.001). The FA values in the patients were significantly lower than in controls, and FDR also showed similar differences; however, they were smaller than those in patients. No significant difference was found between the groups for FA values of the dorsal part of the left cingulum bundle and anterior thalamic radiation. Significant differences were present for ADC values among the groups for the corpus callosum, the dorsal and hippocampal parts of the cingulum, anterior thalamic radiation, and uncinate fasciculus bilaterally (P0.01). The FA and ADC values did not correlate significantly with age or any clinical variables.These findings suggest that BD patients and their FDR show alterations in microstructural integrity of white matter tracts, compared to the healthy population.

It has been shown that significant age difference between a probe and gallery face image can decrease the matching accuracy. If the face images can be normalized in age, there can be a huge impact on the face verification accuracy and thus many novel applications such as matching driver's license, passport and visa images with the real person's images can be effectively implemented. Face progression can address this issue by generating a face image for a specific age. Many researchers have attempted to address this problem focusing on predicting older faces from a younger face. In this paper, we propose a novel method for robust and automatic face progression in totally unconstrained conditions. Our method takes into account that faces belonging to the same age-groups share age patterns such as wrinkles while faces across different age-groups share some common patterns such as expressions and skin colors. Given training images of K different age-groups the proposed method learns to recover K low-rank age and one low-rank common components. These extracted components from the learning phase are used to progress an input face to younger as well as older ages in bidirectional fashion. Using standard datasets, we demonstrate that the proposed progression method outperforms state-of-the-art age progression methods and also improves matching accuracy in a face verification protocol that includes age progression.