Reviewerly/exHarmony huggingface dataset:
Digital Image Correlation for discontinuous displacement measurement using subset segmentation

Deformation measurement is normally achieved by using Digital Image Correlation (DIC) technique when deformation is not discontinuous. However, the presence of discontinuities makes the deformation process very challenging and DIC fails. An innovative technique is proposed in this study which splits the subset (segment) of an image into multiple parts and use segmented subset of the image for correlation process. The performance of the proposed technique is evaluated using different experiments where different types of discontinuities are introduced in the deformation process at different angles and having different discontinuity opening sizes. The obtained results are compared with the recently proposed Discontinuous Digital Image Correlation (DDIC) technique. The results show that the proposed technique is more reliable and having high accuracy which reaches upto 1/100th of a pixel under favorable circumstances.
Asymmetric Controlled Bidirectional Remote Preparation of Single- and Three-Qubit Equatorial State in Noisy Environment

We present an asymmetric controlled bidirectional remote preparation scheme under the control of Charlie, where Alice transmits an arbitrary single-qubit equatorial state to Bob and Bob transmits an arbitrary three-qubit equatorial state to Alice. First, the quantum channel is constructed by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hadamard</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CNOT</i> operations. Second, we consider our scheme in the noiseless environment and construct the appropriate measurement bases. In addition, the participants can recover the prepared states through implementing the corresponding recovery operations determinately. Third, we discuss our scheme in five types of noisy environments (amplitude-damping, phase-damping, bit-flip, phase-flip, and depolarizing noisy environment). The fidelities of the output states are calculated, which depend on the coefficients of the prepared states and the decoherence rates. Finally, some discussions and conclusions are given. Compared with previous schemes, our scheme does not need additional operations or auxiliary qubits. So it is slightly more efficient.
Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network

The PSNR and MSE are the computationally simplest and thus most widely used measures for image quality, although they correlate only poorly with perceived visual quality. More accurate quality models that rely on processing on both the reference and distorted image are potentially difficult to integrate in time-critical communication systems where computational complexity is disadvantageous. This paper derives the concept of distortion sensitivity as a property of the reference image that compensates for a given computational quality model a potential lack of perceptual relevance. This compensation method is applied to the PSNR and leads to a local weighting scheme for the MSE. Local weights are estimated by a deep convolutional neural network and used to improve the PSNR in a computationally graceful distribution of computationally complex processing to the reference image only. The performance of the proposed estimation approach is evaluated on LIVE, TID2013 and CSIQ databases and shows comparable or superior performance compared to benchmark image quality measures.
Coarse-to-Fine: A RNN-Based Hierarchical Attention Model for Vehicle Re-identification

Vehicle re-identification is an important problem and becomes desirable with the rapid expansion of applications in video surveillance and intelligent transportation. By recalling the identification process of human vision, we are aware that there exists a native hierarchical dependency when humans identify different vehicles. Specifically, humans always firstly determine one vehicle's coarse-grained category, i.e., the car model/type. Then, under the branch of the predicted car model/type, they are going to identify specific vehicles by relying on subtle visual cues, e.g., customized paintings and windshield stickers, at the fine-grained level. Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification. RNN-HA consists of three mutually coupled modules: the first module generates image representations for vehicle images, the second hierarchical module models the aforementioned hierarchical dependent relationship, and the last attention module focuses on capturing the subtle visual information distinguishing specific vehicles from each other. By conducting comprehensive experiments on two vehicle re-identification benchmark datasets VeRi and VehicleID, we demonstrate that the proposed model achieves superior performance over state-of-the-art methods.
Recurrent Metric Networks and Batch Multiple Hypothesis for Multi-Object Tracking

Multi-object tracking aims to recover the object trajectories, given multiple detections in video frames.Object feature extraction and similarity metric are the two keys to reliably associate trajectories.In this paper, we propose the recurrent metric network (RMNet), a convolutional neural network-recurrent neural network-based similarity metric framework for the multi-object tracking.Given a reference object, the RMNet takes as input random positive and negative detections and outputs similarity scores over time.The RMNet handles the long-term temporal object variations and false object detections by its long-short memory units.With the scores from RMNet, we introduce a batch multiple hypothesis (BMH) strategy, a simple yet efficient data association method for the batch multi-object tracking.BMH generates a hypothesis tree for each object with a dual-threshold hypothesis generation approach and, then, selects the best branch (or hypothesis) for each object as the batch tracking result.Specially, we model the hypothesis selection as a 0-1 programming problem and introduce a reward function to re-find the objects in case of missing detection.We evaluate our RMNet and BMH strategy on several popular datasets: 2DMOT2015, PETS2009, TUD, and KITTI.We achieve a performance comparable or superior to those of the state-of-theart methods.
<i>Network Attack Surface</i>: Lifting the Concept of Attack Surface to the Network Level for Evaluating Networks’ Resilience Against Zero-Day Attacks

The concept of attack surface has seen many applications in various domains, e.g., software security, cloud security, mobile device security, Moving Target Defense (MTD), etc. However, in contrast to the original attack surface metric, which is formally and quantitatively defined for a software, most of the applications at higher abstraction levels, such as the network level, are limited to an intuitive and qualitative notion, losing the modeling power of the original concept. In this paper, we lift the attack surface concept to the network level as a formal security metric for evaluating the resilience of networks against zero day attacks. Specifically, we first develop novel models for aggregating the attack surface of different network resources. We then design heuristic algorithms to estimate the network attack surface while reducing the effort spent on calculating attack surface for individual resources. Finally, the proposed methods are evaluated through experiments.
An anatomy for neural search engines

In this work, we explore the application of modern deep learning techniques to build a neural model centric search engine. We conduct an in-depth discussion under several quantitative and qualitative criteria, comparing the trade-offs of adopting the proposed neural architecture against the successful and mature traditional information retrieval techniques. We show that a full neural architecture, which employs neural models both in the retrieval and ranking phases, offers good scalability, predictability and evolution properties, and discuss under which conditions one can achieve state-of-the-art results. We conclude that deep learning centric systems still require significant more effort to implement and deploy and demand more computational resources, but this work, together with several others in the research community, sheds a light into that path.
Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning

With the rapid development of wearable devices and technologies, hand segmentation remains a less explored direction in egocentric vision, which is very important for activity recognition, rehabilitation, robot self-learning, etc. To overcome the high cost of auxiliary equipment and pixel-level annotations, we present an un-supervised hand segmentation method for egocentric images. Firstly, a fully convolutional neural network (FCN) is pre-trained in source dataset containing pixel-level annotations. Then, in target dataset without labels, the network is re-trained with optimized masks, which are produced by modified local and global consistency learning (LLGC) based on pre-segmentation and superpixel features. Finally, hand segmentation is realized in an alternative way. Furthermore, to balance segmentation accuracy and the cost on labeling, we propose a new semi-supervised image segmentation framework with three sub-nets based on the optimized noisy masks and a small number of clean labeled data. Experimental results in two target datasets indicate that the proposed methods could achieve better performance than other methods.
Depth matrix and adaptive Bayes classifier based dynamic hand gesture recognition

A sequence of apparently ad-hoc hand postures can generate meaningful dynamic gestures which can be utilized in interface controls for computer, television, or games. In order to develop deployable systems with these gestures, selected descriptors should be fast enough to meet the live recognition requirements. This paper proposes framework for a practical system capable of recognizing continuous dynamic gestures characterized by short-duration posture sequences. A depth-based modification to the shape matrix is devised to describe hand silhouettes, which gives a faster alternative to region-based descriptors. Postures are recognized using depth matrix and 1-nearest neighbor strategy. Posture sequence labels are predicted by a dynamic naive Bayes classifier which works in association with an adaptive windowing mechanism. The conducted experiments report up to 96.2% accurate results with mean accuracy of 95.2% on dynamic gesture dataset. Depth matrix computation takes a maximum of 2ms time.
Numerical and experimental study on deformation of 3D-printed polymeric functionally graded plates: 3D-Digital Image Correlation approach

This paper presents a novel experimental and simulation investigation of quasi-static transverse deformation of 3D printed polymeric Functionally Graded (FG) plates, which obey the distribution of material properties through the length. Graded solid elements with continuous property distribution at different Gauss points were implemented by a user material subroutine (UMAT) in ABAQUS Finite Element (FE) software. In order to validate the proposed graded FE solutions, the original experimental deflection measurements using 3D-Digital Image Correlation (DIC) technique performed to capture transverse deformation of designed and manufactured 3D printed polymeric FG plates. DIC technique involved tracking the motion of geometric features on a specimen surface over the course of an experiment to generate the displacement field experimentally. It was found that the deviation between FE and experimental out of plane deflection for nonlinear FG plates is higher than that of linear FG plates, which is potentially due to the high gradient distribution of the material inclusions. It can be concluded that the presented 3D-digtal image correlation technique provides for transverse deformation of polymeric FG plates. The deflection contours for the FG plate are not symmetric, differences being more prominent for the linear high stiffness ratio combination. The outcomes of this study can be applied to determine the optimum material distribution to produce a controlled-stiffness polymeric FG plate corresponding to prescribed structural characteristics.
An Anonymous Off-Blockchain Micropayments Scheme for Cryptocurrencies in the Real World

Blockchain, as a secured, trusted, and decentralized architecture, is used to create secure and tamper-proof payment schemes, which can serve economies and societies without trusted parties. However, the transparency and traceability of blockchain severely restrict the anonymity of participants in the real world, which will cause participants' privacy leakage. Toward this end, in this paper, an anonymous off-blockchain micropayments scheme (AOM) is proposed for cryptocurrencies in the real world. In AOM, a payee receives micropayments from an “honest-but-curious” intermediary T by solving puzzles which are generated based on the standard RSA assumption. Meanwhile, T also receives micropayments from the payers by payee's solutions and T will randomly select the inputs of the merging transaction Tmer. In order to improve service efficiency of T and resist denial of service attack, one of the outputs of T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mer</sub> is paid for T as a service fee. Besides, AOM simultaneously ensures the correctness and fairness of transactions. Finally, from the analyses of property and security, AOM has strong unlinkability, ability for anti-attacks and unforgeability.
Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

Many recent variational optical flow methods are not robust for illumination variance, and they only consider local image relation in terms of illumination. In this paper, we propose a new efficient illumination-invariance total variation optical flow method called the weighted regularization transform, which uses and optimizes the Weber's Law. Our method exploits unequal probability as the weight that has non-local information to estimate stable optical flow despite illumination changes. The proposed method uses a coarse-to-fine pyramid model to reduce the influence on the data term from illumination. Then, an energy optimization procedure is introduced to constrain the minimization of the data term with the non-local regularization. Experimentation with the proposed method has been performed on three optical flow datasets and a face liveness detection database, which have challenging illumination variations, and the results demonstrate that the proposed method is quite robust with respect to variations in illumination.
Shadow detection and removal from images using machine learning and morphological operations

In this study, the authors present a system for shadow detection and removal from images using machine learning technique. A machine learning algorithm ESRT (enhanced streaming random tree) model is proposed. The image is converted to HSV and 26 parameters are taken as image measurements. A dataset in Attribute Relation File Format is created for shadow and non-shadow images. The algorithm is trained using the training dataset and tested using the test dataset. Segmentation is performed. The similar threshold homogeneity pixel is grouped. Colour chromaticity is used to remove cast shadow. Morphological processing is performed to remove the shadow from the image. The algorithm shows better detection rate and accuracy compared with Bayesian classifiers available in WEKA.
WassRank

Learning to rank has been intensively studied and has shown great value in many fields, such as web search, question answering and recommender systems. This paper focuses on listwise document ranking, where all documents associated with the same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learning the optimal ranking function that achieves the minimum Wasserstein distance. Specifically, given the query level predictions and the ground truth labels, we first map them into two probability vectors. Analogous to the optimal transport problem, we view each probability vector as a pile of relevance mass with peaks indicating higher relevance. The listwise ranking loss is formulated as the minimum cost (the Wasserstein distance) of transporting (or reshaping) the pile of predicted relevance mass so that it matches the pile of ground-truth relevance mass. The smaller the Wasserstein distance is, the closer the prediction gets to the ground-truth. To better capture the inherent relevance-based order information among documents with different relevance labels and lower the variance of predictions for documents with the same relevance label, ranking-specific cost matrix is imposed. To validate the effectiveness of WassRank, we conduct a series of experiments on two benchmark collections. The experimental results demonstrate that: compared with four non-trivial listwise ranking methods (i.e., LambdaRank, ListNet, ListMLE and ApxNDCG), WassRank can achieve substantially improved performance in terms of nDCG and ERR across different rank positions. Specifically, the maximum improvements of WassRank over LambdaRank, ListNet, ListMLE and ApxNDCG in terms of [email protected] are 15%, 5%, 7%, 5%, respectively.
Class Agnostic Image Common Object Detection

Learning similarity of two images is an important problem in computer vision and has many potential applications. Most of the previous works focus on generating image similarities in three aspects: global feature distance computing, local feature matching, and image concepts comparison. However, the task of directly detecting the class agnostic common objects from two images has not been studied before, which goes one step further to capture image similarities at the region level. In this paper, we propose an end-to-end image Common Object Detection Network (CODN) to detect class agnostic common objects from two images. The proposed method consists of two main modules: locating module and matching module. The locating module generates candidate proposals of each two images. The matching module learns the similarities of the candidate proposal pairs from two images, and refines the bounding boxes of the candidate proposals. The learning procedure of CODN is implemented in an integrated way and a multi-task loss is designed to guarantee both region localization and common object matching. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. The experimental results validate the effectiveness of the proposed method.
Making and negotiating value: design and collaboration with community led groups

Value in collaborative design research and practice can be understood fundamentally as relationships, materials, processes, contexts, and outcomes that are subjects of and for negotiation. We argue for conceptions of value that move beyond traditional 'outcomes' based measurements to reimagine and rearticulate value itself as co-created, emerging from negotiation, relationality and immersion in specific contexts. These understandings of value, we argue, are not rooted in or always knowable through designers' experiences, even as designers participate in creating them. Using case studies from our research we suggest that value in design collaboration emerges as a question: value to whom, and to what end? We propose that addressing these questions ethically through co-design requires actively engaged, grounded work with collaborators based in three principles: being present for the work, participant making, and co-creating capacity for collaborators to 'go off and do their thing'.
Pokémon Go-ing or staying: exploring the effect of age and gender on augmented reality game player experiences in public spaces

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A new method for pdf steganography in justified texts

To remove the ragged edges of the text, some of the text editors increase the size of some between words spaces. This process is named as “Justify”. Different editors use different algorithms for justifying the texts. In this paper we introduce a hiding method which is based on justified text in a pdf file. In this proposed method, first we compress the secret message by Huffman coding. Then we choose some special lines of the cover as host lines. The embedding operation occurs by the replacement of the added spaces by the normal spaces of the host lines. Finally we re-pdf the file and send it. This scheme has a key, and in each use we can choose a new key for increasing the security of the communications. In this proposed method we can embed more information in a cover-text compared with several other text steganography schemes. Also the size of the file of the cover is not changed in this method. The next advantage of this method is that the parties of secret communication do not need to send electronic files and furthermore this method is printable.
Medical Images Analysis Based on Multilabel Classification

In the last years, a lot of literature has provided considerable support for multilabel classification in machine learning. It means that each sample or instance belongs to more than one class label simultaneously. Therefore, it represents complex objects that have multiple meanings. It helps in capturing more information by labeling some basic and hidden patterns. In this respect, multilabel classification is very useful in medical data analysis. It addresses the problem of diagnosis, surgery, anatomy, disease progress, analysis, and teaching purposes in medicine. There are many patients have many diseases at the same time, maybe in the same organ, such as ocular diseases. On the other side, the multilabel classification is a challenging issue by nature. This is due to high dimensionality, sparseness, and imbalance of available data. Some problems with labels are raised, such as label dependency, locality, interlabel diversity, and similarity. Therefore, our survey introduces significant topics of the multilabel classification in medical image analysis field. Notably, most of the literature did not show how multilabel classification affect the medical image analysis. In this chapter, we presented the different examples of medical image classification by the multilabel methods. We present the detailed analysis and discussions of the literature findings. The performance of the methods is compared on five publicly available data sets such as yeast, scene, genebase, corel5k and BibTex of multilabel classification using famous measures. Moreover, we intend to give the researcher a computer-aided CAD system framework for the existing multilabel classification research.