Reviewerly/exHarmony huggingface dataset:
Face Sketch Synthesis in the Wild via Deep Patch Representation-Based Probabilistic Graphical Model

This paper considers the problem of face sketch synthesis in the wild, which transforms a face photo into a face sketch. Face sketch synthesis is widely applied in law enforcement as well as digital entertainment fields. However, the existing methods either focus on hand-crafted techniques where prior human experience is relied on or adopt deep learning techniques as an end-to-end framework, where facial details cannot be well represented. In this paper, we propose a novel approach for face sketch synthesis in the wild via a deep patch representation-based probabilistic graphical model (DeepPGM). A Siamese network is constructed to extract deep patch representation from a raw facial patch, where the representative detail information for robust face sketch synthesis can be exploited. The generated deep patch representation and facial image patches are then optimally combined through a probabilistic graphical model. The proposed DeepPGM approach not only outperforms the state-of-the-art on public face sketch datasets but also can cope with forensic photos in the wild conditions, including varying lightings, poses, occlusions, skin colors, and ethnic origins. The superiority of the proposed method is demonstrated by extensive experiments on two public face sketch datasets and real-world forensic photos in the wild.
Comparison of Contrast-Enhanced Mammography With Conventional Digital Mammography in Breast Cancer Screening: A Pilot Study

To perform a pilot evaluation of contrast-enhanced mammography (CEM) for screening to determine whether it can improve accuracy and reader confidence in diagnosis.This institutional review board-approved reader study was comprised of 64 de-identified CEM cases acquired from December 1, 2014, to June 7, 2016, including 48 negative, 5 biopsy-proven benign, and 11 biopsy-proven malignancies. Negative cases were followed for at least 2 years without evidence of cancer. Ten breast imagers of varying experience first rated the low-energy (LE) mammogram and then the CEM examination using BI-RADS categories and a 5-point Likert scale for confidence in diagnosis.There were 635 out a total possible 640 complete reader interpretations included in this analysis. The remaining five incomplete interpretations were excluded. Median sensitivity and specificity improved with the addition of CEM (sensitivity: 0.86 [95% confidence interval {CI}: 0.74-0.95] versus 1 [95% CI: 0.83-1.00], specificity: 0.85 [95% CI: 0.64-0.94] versus 0.88 [95% CI: 0.80-0.92]). Individual receiver operating characteristic curves showed significant improvement with CEM (mean area under the curve increase = 0.056 [95% CI: 0.015-0.097], P = .002). The addition of CEM significantly improved average confidence in 5 of 10 readers when compared with LE (P < .0001) and improved pooled confidence across all tissue density categories, except the almost entirely fatty category. There was a trend toward improved confidence with increasing tissue density with CEM. Degree of background parenchymal enhancement did not affect readers' level of improvement in confidence when interpreting CEM.CEM improved reader performance and confidence compared with viewing only LE, suggesting a role for CEM in breast cancer screening for which larger trials are warranted.
Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of Computer-aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods.
Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm

We obtain a characterization of all wavelets leading to analytic wavelet transforms (WT). The characterization is obtained as a by-product of the theoretical foundations of a new method for wavelet phase reconstruction from magnitude-only coefficients. The cornerstone of our analysis is an expression of the partial derivatives of the continuous WT, which results in phase-magnitude relationships similar to the short-time Fourier transform (STFT) setting and valid for the generalized family of Cauchy wavelets. We show that the existence of such relations is equivalent to analyticity of the WT up to a multiplicative weight and a scaling of the mother wavelet. The implementation of the new phaseless reconstruction method is considered in detail and compared to previous methods. It is shown that the proposed method provides significant performance gains and a great flexibility regarding accuracy versus complexity. Additionally, we discuss the relation between scalogram reassignment operators and the wavelet transform phase gradient and present an observation on the phase around zeros of the WT.
Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates

Evaluating whole-body motion is challenging because of the articulated nature of the skeleton structure. Each joint moves in an unpredictable way with uncountable possibilities of movements direction under the influence of one or many of its parent joints. This paper presents a method for human motion quantification via three-dimensional (3-D) body joints coordinates. We calculate a set of metrics that influence the joints movement considering the motion of its parent joints without requiring prior knowledge of the motion parameters. Only the raw joints coordinates data of a motion sequence are needed to automatically estimate the transformation matrix of the joints between frames. We also consider the angles between limbs as a fundamental factor to follow the joints directions. We classify the joints motion as global motion and local motion. The global motion represents the joint movement according to a fixed joint, and the local motion represents the joint movement according to its first parent joint. In order to evaluate the performance of the proposed method, we also propose a comparison algorithm between two skeletons motions based on the quantified metrics. We measured the comparative similarity between the 3-D joints coordinates on Microsoft Kinect V2 and UTD-MHAD dataset. User studies were conducted to evaluate the performance under different factors. Various results and comparisons have shown that our method effectively quantifies and evaluates the motion similarity.
A comprehensive study of the affective and physiological responses induced by dynamic virtual reality environments

Abstract Previous studies showed that virtual reality (VR) environments can affect emotional state and cause significant changes in physiological responses. Aside from these effects, inadvertently induced cybersickness is a notorious problem faced in VR. In this study, to further investigate the effects of virtual environments (VEs) with different context, three dynamic VEs were created. Each VE had a particular purpose: evoking no emotion in Campfire (CF), unpleasant emotions in Hospital (HH), and cybersickness symptoms in Roller Coaster (RC). We made use of objective measurements of physiological responses such as pupil dilation, blinks, fixations, saccades, and heart rate, as well as subjective self‐assessments via pre‐ and post‐VE session questionnaires. While previous studies investigate different subsets of these measures, our study makes a comprehensive analysis of them jointly in dynamic VEs. The results of the study indicate that cybersickness produced higher saccade mean speed, whereas unpleasant context caused higher fixation count, saccade rate, and pupil dilation. Moreover, CF decreased anxiety, whereas HH and RC increased it and they also decreased comfort. Participants felt cybersickness in all VEs even in CF which is designed to minimize the effects.
Turbospeedz: Double Your Online SPDZ! Improving SPDZ Using Function Dependent Preprocessing

Secure multiparty computation allows a set of mutually distrusting parties to securely compute a function of their private inputs, revealing only the output, even if some of the parties are corrupt. Recent years have seen an enormous amount of work that drastically improved the concrete efficiency of secure multiparty computation protocols. Many secure multiparty protocols work in an “offline-online” model. In this model, the computation is split into two main phases: a relatively slow “offline phase”, which the parties execute before they know their input, and a fast “online phase”, which the parties execute after receiving their input. One of the most popular and efficient protocols for secure multiparty computation working in this model is the SPDZ protocol (Damgård et al., CRYPTO 2012). The SPDZ offline phase is function independent, i.e., does not require knowledge of the computed function at the offline phase. Thus, a natural question is: can the efficiency of the SPDZ protocol be improved if the function is known at the offline phase? In this work, we answer the above question affirmatively. We show that by using a function dependent preprocessing protocol, the online communication of the SPDZ protocol can be brought down significantly, almost by a factor of 2, and the online computation is often also significantly reduced. In scenarios where communication is the bottleneck, such as strong computers on low bandwidth networks, this could potentially almost double the online throughput of the SPDZ protocol, when securely computing the same circuit many times in parallel (on different inputs). We present two versions of our protocol: Our first version uses the SPDZ offline phase protocol as a black-box, which achieves the improved online communication at the cost of slightly increasing the offline communication. Our second version works by modifying the state-of-the-art SPDZ preprocessing protocol, Overdrive (Keller et al., Eurocrypt 2018). This version improves the overall communication over the state-of-the-art SPDZ.
Security analysis and improvement of an arbitrated quantum signature scheme

We analyze the security of Jiang et al.’s arbitrated quantum signature scheme based on local indistinguishability of orthogonal product states. First, Jiang et al.’s scheme cannot resist against the forgery attack. Second, it is insecure against disavowal attack. Then, an improved arbitrated quantum signature scheme is proposed. The new scheme overcomes the security drawbacks of Jiang et al.’s scheme and it is secure against forgery attack and disavowal attack. What is more, it is more efficient than the old scheme. On the other hand, our quantum signature scheme is based on product states, which can be obtained straightforwardly, thus our scheme is easier to implement than the schemes using cluster states.
Knowledge fusion patterns: A survey

The survey analyzes, summarizes, and classifies knowledge fusion patterns reported in different studies. It provides overview of the patterns explicitly declared in the found studies and proposes some patterns that have been recognized as such. Namely, some knowledge fusion models are considered as patterns. The patterns are classified from the perspective of the types of resources that provide knowledge for the fusion process. Two types of resources are distinguished: knowledge worker and knowledge repository. Three groups of patterns are differentiated: (1) patterns fusing knowledge stored in knowledge repositories; (2) patterns fusing knowledge of knowledge workers; and (3) patterns fusing knowledge of knowledge workers and knowledge accumulated in repositories. The patterns are specified in terms of the pattern names, the inputs and outputs, the methods supporting knowledge fusion, and the references to the sources introducing these patterns.
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness

Defending adversarial attack is a critical step towards reliable deployment of deep learning empowered solutions for industrial applications. Probabilistic adversarial robustness (PAR), as a theoretical framework, is introduced to neutralize adversarial attacks by concentrating sample probability to adversarial-free zones. Distinct to most of the existing defense mechanisms that require modifying the architecture/training of the target classifier which is not feasible in the real-world scenario, e.g., when a model has already been deployed, PAR is designed in the first place to provide proactive protection to an existing fixed model. ShieldNet is implemented as a demonstration of PAR in this work by using PixelCNN. Experimental results show that this approach is generalizable, robust against adversarial transferability and resistant to a wide variety of attacks on the Fashion-MNIST and CIFAR10 datasets, respectively.
Weakly Supervised Image Classification Through Noise Regularization

Weakly supervised learning is an essential problem in computer vision tasks, such as image classification, object recognition, etc., because it is expected to work in the scenarios where a large dataset with clean labels is not available. While there are a number of studies on weakly supervised image classification, they usually limited to either single-label or multi-label scenarios. In this work, we propose an effective approach for weakly supervised image classification utilizing massive noisy labeled data with only a small set of clean labels (e.g., 5%). The proposed approach consists of a clean net and a residual net, which aim to learn a mapping from feature space to clean label space and a residual mapping from feature space to the residual between clean labels and noisy labels, respectively, in a multi-task learning manner. Thus, the residual net works as a regularization term to improve the clean net training. We evaluate the proposed approach on two multi-label datasets (OpenImage and MS COCO2014) and a single-label dataset (Clothing1M). Experimental results show that the proposed approach outperforms the state-of-the-art methods, and generalizes well to both single-label and multi-label scenarios.
RaSQL

Thanks to a simple SQL extension, Recursive-aggregate-SQL (RaSQL) can express very powerful queries and declarative algorithms, such as classical graph algorithms and data mining algorithms. A novel compiler implementation allows RaSQL to map declarative queries into one basic fixpoint operator supporting aggregates in recursive queries. A fully optimized implementation of this fixpoint operator leads to superior performance, scalability and portability. Thus, our RaSQL system, which extends Spark SQL with the before-mentioned new constructs and implementation techniques, matches and often surpasses the performance of other systems, including Apache Giraph, GraphX and Myria.
Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked.The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use.The MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed.A total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes.There is a gap in the literature between CAD's well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.
ActiveHARNet

Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR). HAR using mobile- and wearable-based deep learning algorithms have been on the rise owing to the advancements in pervasive computing. However, there are two other challenges that need to be addressed: first, the deep learning model should support on-device incremental training (model updation) from real-time incoming data points to learn user behavior over time, while also being resource-friendly; second, a suitable ground truthing technique (like Active Learning) should help establish labels on-the-fly while also selecting only the most informative data points to query from an oracle. Hence, in this paper, we propose ActiveHARNet, a resource-efficient deep ensembled model which supports on-device Incremental Learning and inference, with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks using dropout. This is combined with suitable acquisition functions for active learning. Empirical results on two publicly available wrist-worn HAR and fall detection datasets indicate that ActiveHARNet achieves considerable efficiency boost during inference across different users, with a substantially low number of acquired pool points (at least 60% reduction) during incremental learning on both datasets experimented with various acquisition functions, thus demonstrating deployment and Incremental Learning feasibility.
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.
Protecting IoT-environments against Traffic Analysis Attacks with Traffic Morphing

Traffic analysis attacks allow an attacker to infer sensitive information about users by analyzing network traffic of user devices. These attacks are passive in nature and are difficult to detect. In this paper, we demonstrate that an adversary, with access to upstream traffic from a smart home network, can identify the device types and user interactions with IoT devices, with significant confidence. These attacks are practical even when device traffic is encrypted because they only utilize statistical properties, such as traffic rates, for analysis. In order to mitigate the privacy implications of traffic analysis attacks, we propose a traffic morphing technique, which shapes network traffic thus making it more difficult to identify IoT devices and their activities. Our evaluation shows that the proposed technique provides protection against traffic analysis attacks and prevent privacy leakages for smart home users.
Underwater image restoration based on diffraction bounded optimization algorithm with dark channel prior

The restoration of underwater images is challenging due to the turbid nature of water. The effects of diffraction due to the varying refractive index of water medium, absorption and scattering properties of water and the distance between the image spot to the camera, can lead to geometric distortions and blur. Many algorithms exist so far to restore the quality of underwater images. The effect of turbulence in underwater scenarios areas are challenging for most restoration algorithms. Although the metric gauge for the restoration of the underwater scene is still under evaluation, a new algorithm based on the Diffraction Bounded Image Formation Model (DB-IFM) is proposed. The diffraction correction restores the image based on the diffraction-bounded restoration. The dark channel of the image assessed with various transmission maps based on Dark Channel Prior (DCP) algorithm. For a broad set of data with varying luminance, the outcomes are looked at both subjectively and quantitatively by enhanced restoration.
FPGA-Based Implementation of a Real-Time Object Recognition System Using Convolutional Neural Network

High computational complexity and power consumption makes convolutional neural networks (CNNs) ineligible for real-time embedded applications. In this brief, we introduce a low power and flexible platform as a hardware accelerator for CNNs. The proposed architecture is fully configurable by a software library so that it can perform different CNN models with a reconfigurable hardware. The hardware accelerator is evaluated on a ZC706 evaluation board. We make use of the AlexNet architecture in a real-time object recognition application to demonstrate the effectiveness of the proposed CNN accelerator. The results show that the performance rates of 198.1 GOP/s using 512 DSP blocks and 23.14 GOP/s using 64 DSP blocks are achievable for the convolution and fully connected layers, respectively. Moreover, images are processed at 82 frames/s, which is significantly higher than existing implementations.