Reviewerly/exHarmony huggingface dataset:
Image DAEs based on residual entropy maximum

Image denoising under low signal-to-noise ratio (SNR) and non-Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto-encoders for image denoising. Different from other priors based methods, the denoising auto-encoders (DAEs) can learn end-to-end mappings from noisy images to the target ones. This study research statistical features of image residual between the restored images and target images. According to the maximum entropy principle, the training loss function of the ordinary DAEs was modified with residual statistics as the constraint condition, and an improved training algorithm was proposed based on augmented Lagrange function method. Thus, the quality of restored image can be improved through removing image information from residual more efficiently. Experiments show not only the denoising effects of improved DAEs is superior to the original mean-square-error loss function DAEs in both peak SNR and Riesz feature similarity metric indexes, but also has the ability to suppress the different types of noises with different levels through a single model.
Towards Automated Breast Mass Classification using Deep Learning Framework

Due to high variability in shape, structure and occurrence; the non-palpable breast masses are often missed by the experienced radiologists. To aid them with more accurate identification, computer-aided detection (CAD) systems are widely used. Most of the developed CAD systems use complex handcrafted features which introduce difficulties for further improvement in performance. Deep or high-level features extracted using deep learning models already have proven its superiority over the low or middle-level handcrafted features. In this paper, we propose an automated deep CAD system performing both the functions: mass detection and classification. Our proposed framework is composed of three cascaded structures: suspicious region identification, mass/no-mass detection and mass classification. To detect the suspicious regions in a breast mammogram, we have used a deep hierarchical mass prediction network. Then we take a decision on whether the predicted lesions contain any abnormal masses using CNN high-level features from the augmented intensity and wavelet features. Afterwards, the mass classification is carried out only for abnormal cases with the same CNN structure. The whole process of breast mass classification including the extraction of wavelet features is automated in this work. We have tested our proposed model on widely used DDSM and INbreast databases in which mass prediction network has achieved the sensitivity of 0.94 and 0.96 followed by a mass/no-mass detection with the area under the curve (AUC) of 0.9976 and 0.9922 respectively on receiver operating characteristic (ROC) curve. Finally, the classification network has obtained an accuracy of 98.05% in DDSM and 98.14% in INbreast database which we believe is the best reported so far.
NetStor: Network and Storage Traffic Management for Ensuring Application QoS in a Hyperconverged Data-Center

For the rapid increase in resource requirements in large scale Data Centers (DCs), enterprises have brought hyperconverged architecture where the storage pool is built up by the individual storage components associated with different servers, and it is shared among all the Virtual Machines (VMs) or containers through a common network infrastructure. Due to the sharing of network bandwidth among the application generated network traffic and the storage traffic from shared storage infrastructure, quality of service (QoS) performances of networking and storage intensive applications are affected, which further impacts VM or container migrations with dynamic workload scenarios. In this article, we propose NetStor to ensure QoS for various collocated network and storage intensive workloads over a hyperconverged architecture and ensure QoS during VM or container migration. NetStor uses a workload estimation strategy for VMs and containers, and applies the strategic decisions for resource allocation and migration based on environmental learning and workload characterization. NetStor supports a dynamic QoS provisioning that is workload-agnostic catering to both VMs and containers, and is unique to the best of our knowledge. We have implemented NetStor over a hyperconverged DC architecture on a testbed and found that NetStor can enhance network performance significantly compared to other related mechanisms discussed in the literature.
Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.
SSIM Prediction for H.265/HEVC based on Convolutional Neural Networks

In signal compression, distortion information is significant for rate distortion optimization. In this paper, we propose a convolutional neural network (CNN) to predict distortion information for H.265/HEVC. With the strong representation power of CNN, structural similarity (SSIM) maps indicating distortion information can be predicted directly in an end-to-end, pixel-to-pixel way. Different from traditional CNNs which focus on learning one-to-one mappings from input to output, we show that our CNN model can predict SSIM maps conditioned on quantization parameters (QPs), realizing one-to-many mappings. To construct our CNN network, QP labels are designed as conditions to feed the CNN model. We also apply symmetrical network architecture and multi-level feature fusion method to ensure our network can utilize both high-level semantic features and low-level structure features. The experiments on MS COCO database demonstrate the effectiveness of our CNN-based method for SSIM prediction.
Telecytology rapid on‐site evaluation: Diagnostic challenges, technical issues and lessons learned

Abstract Introduction Rapid on‐site evaluation ( ROSE ) has been widely used to improve diagnostic adequacy and facilitate specimen triage. Telecytology ROSE has gained popularity recently and shown high concordance with traditional ROSE . However, telecytology involves multiple personnel and technical devices that could introduce additional errors. The aim of this paper is to share errors encountered and lessons learned since employing telecytology for ROSE at our institution. Methods The laboratory information system was searched for all documented telecytology ROSE errors from 2017 to 2019. These errors were subclassified as technical errors, cytotechnologist‐related errors and pathologist‐related errors. The following details were recorded for each reported event: type of error, reason for error, ROSE diagnosis, final diagnosis and actions taken to avoid future errors. Results Telecytology ROSE errors were documented in 46 (1.3%) sessions. Ten (22%) had technical errors, 13 (28%) were owing to cytotechnologist errors and 23 (50%) were attributed to pathologist interpretation errors. The majority of the technical (90%) and cytotechnologist errors (85%) occurred within the first year of implementation of telecytology. Common ROSE misinterpretation errors included missing microorganisms, misclassifying neuroendocrine tumours as other neoplasms and overcalling malignancy on gastrointestinal endoscopic procedures. Conclusions A variety of errors may occur during telecytology ROSE . While some errors are inevitable (eg, information technology downtime), certain telecytology errors can be reduced by increasing staff familiarity with the system, providing timely feedbacks and taking prompt corrective actions. We recommend establishing a mechanism to document and act upon recorded errors as part of a telecytology quality improvement programme.
ALERT: Adversarial Learning With Expert Regularization Using Tikhonov Operator for Missing Band Reconstruction

The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth's surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model.
Face Recognition and Application of Film and Television Actors Based on Dlib

With the rapid development of film and television industry, it is necessary to find suitable stand-ins for actors, select appropriate actors for different periods of roles, and calculate the acting time of actors. The system detects the actor's face, extracts feature points, calculates face feature vectors, and then obtains the actor's identity information. Based on Dlib, this paper designed and implemented a film actor face recognition system, and designed experiments to verify the effectiveness of the face recognition system. The system has a high recognition rate for relatively clear faces, but the face recognition effect with occlusion needs to be improved.
GAN-based Color Correction for Underwater Object Detection

Underwater images suffer degradation from light propagates through water. The degradation by light propagation not only affects the quality of underwater images but limits the ability of object detection. In this paper, we propose a color correction method by cycle-consistent adversarial networks (CycleGAN) for an improvement of underwater object detection. The proposed CycleGAN is implemented with a polynomial loss function including adversarial loss, cycle-consistency loss, and structural similarity index measure (SSIM) loss. Thereby, underwater images are generated as output images including the content and structure of the input images. Simulation results show that our method achieves an improved 7% mean average precision in underwater object detection, compared with a conventional method.
A Blind Ring Signature Based on the Short Integer Solution Problem

A blind ring signature scheme is a combination of a ring signature and a blind signature, which allows not only any member of a group of signers to sign on a message on behalf of the group without revealing its identity but also the user who possesses the message to blind it before sending to the group to be signed. Blind ring signature schemes are essential components in e-commercial, e-voting etc. In this paper, we propose the first blind ring signature scheme based on lattices. More precisely, our proposed scheme is proven to be secure in random oracle model under the hardness of the short integer solution (SIS) problem.
Near-Infrared Spectroscopic Study of Heavy-Metal-Contaminated Loess Soils in Tongguan Gold Area, Central China

Loess soil is a kind of widespread soil type in northwest China. Human engineering activities such as mining have caused numerous problems related to heavy metal pollution in soils, which threaten people’s health. The band formation mechanism of the near-infrared (NIR) spectral features in loess soils forms the theoretical basis for the study of the soil environment by hyperspectral remote sensing. Some NIR features of loess soils will shift because of the variations of the soil composition and microstructure after they adsorb heavy metal cations. In this study, we focused on the heavy metal adsorption of the illite, smectite, and illite–smectite (I/S) mixed layer in loess soils; evaluated the pollution by Nemerow indexing; applied X-ray diffraction (XRD), mid-infrared (MIR) spectral analysis, and inductively coupled plasma mass spectrometry (ICP-MS); and carefully observed the shift behavior of the MIR and NIR features. Then, the NIR bands were assigned to MIR bands according to the vibration behavior. Furthermore, the relationships between the NIR band positions and the six heavy metal cations as well as the Nemerow index were investigated via multiregression and simple linear correlation methods. Finally, the relationship obtained from the experiments was analyzed using the physical and chemical mechanisms of the heavy metal cations in the clay minerals. These findings may benefit the application of NIR and remote sensing techniques for detecting heavy-metal-polluted soils.
BlockU: Extended usage control in and for Blockchain

Abstract An electronic business transaction among untrusted bodies without consulting a mutually trusted party has remained widely accepted problem. Blockchain resolves this problem by introducing peer‐to‐peer network with a consensus algorithm and trusted ledger. Blockchain originally introduced for cryptocurrency that came with proof‐of‐work consensus algorithm. Due to some performance issues, scientists brought concept of permissioned Blockchain. Hyperledger Fabric is a permissioned Blockchain targeting business‐oriented problems for industry. It is designed for efficient transaction execution over Blockchain with pluggable consensus model; however, there is limitation of rapid application development. Hyperledger introduced a new layer called Hyperledger Composer on top of the Fabric layer, which provides an abstract layer to model the business application readily and quickly. Composer provides a smart contract to extend the functionality and flexibility of Fabric layer and provides a way of communication with other systems to meet business requirements. Hyperledger Composer uses role‐based access control (RBAC) model to secure access to its valuable assets. However, RBAC is not enough because many business deals require continuous assets monitoring. Our proposed model, BlockU, covers all possible access control models required by a business. BlockU can monitor assets continuously during transactions and updates attributes accordingly. Moreover, we incorporate hooks in Hyperledger Composer to implement extended permission model that provides extensive permission management capability on an asset. Subsequently, our proposed enhanced access control model is implemented with a minimal change to existing Composer code base and is backward compatible with the current security mechanism.
Discussion on the Application of RFID Technology in Medical Waste Management

To explore the application of RFID technology in medical waste management, and to clarify the positive effect of RFID technology on medical waste disposal. Methods: through the literature research analysis of the current situation of medical waste disposal and understanding about the RFID technology, combined with descriptive study to clarify the advantages and disadvantages of RFID technology and the technology in the process of regulation of medical waste concrete application process. Results: combination of RFID technology and video monitoring technology and large data cloud storage platform is 99% + / - 0.5 in medical waste management to avoid the loss of medical waste or illegally recycling. Conclusion: RFID technology can play a real and effective role in medical waste treatment.
Text Extraction and Restoration of Old Handwritten Documents

Image restoration is a crucial computer vision task. This chapter describes two novel methods for the restoration of old, degraded handwritten documents using a deep neural network. In addition, a small-scale dataset consisting of images of 26 heritage letters is introduced. The ground truth data to train the desired network is generated semi-automatically, involving a pragmatic combination of colour transformation, Gaussian mixture model-based segmentation and correction by using mathematical morphological operators. In the first approach, a deep neural network has been used for text extraction from the document image. The background is then reconstructed based on a Gaussian mixture model. Note that a Gaussian mixture model requires setting parameters manually; to alleviate this problem, we propose a second approach where both the background reconstruction and the foreground extraction (i.e. extracting text with its original colour) are achieved using a deep neural network. Experiments demonstrate that the proposed systems perform well on handwritten document images with severe degradation, even when trained with a small dataset. Hence, the proposed methods are ideally suited for digital heritage preservation repositories where the number of samples is low. It is worth mentioning that these methods can be extended easily for printed degraded documents as well.
Assessment of Supervised Learning Algorithms for Irony Detection in Online Social Media

The social media has become one of the most widely used communication tools for people to share their opinions with others. The irony, with a simple definition, is the creative use of language, and it has recently attracted the attention of computer scientists. There is a vast amount of raw data, collected from social media resources, containing ironical statements. It is very difficult to analyze the rapidly growing data manually. Additionally, the character size limitations and typographical errors of online social media tools make the traditional methods of classification insufficient for detection task. In this study, the detection of irony in online social media is modeled as a classification problem, and the success of supervised machine learning methods in real data is assessed. Bayesian Network (BayesNet), OneR, Stochastic Gradient Descent (SGD), Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), Radial Basis Function Networks (RBF), Voted Perceptron, IBk, Randomizable Filtered Classifier (RFC), Isolation Forest, Fuzzy Lattice Reasoning (FLR), and Bagging algorithms are applied for the first time on irony detection problem and a comprehensive evaluation is provided.
Artificial neural networks with a signed-rank objective function and applications

In this paper, we propose to analyze artificial neural networks using a signed-rank objective function as the error function. We prove that the variance of the gradient of the learning process is bounded as a function of the number of patterns and/or outputs, therefore preventing the gradient explosion phenomenon. Simulations show that the method is particularly efficient at reproducing chaotic behaviors from biological models such as the Logistic and Ricker models. In particular, the accuracy of the learning process is improved relatively to the least squares objective function in these cases. Applications in regression settings on two real datasets, one small and the other relatively large are also considered.
Dynamic Hand Gesture Recognition for Robot Arm Teaching based on Improved LRCN Model

In this research, we focus on finding a new method of human-robot interaction in industrial environment. A vision-based dynamic hand gestures recognition system has been proposed for robot arm picking task. 8 dynamic hand gestures are captured for this task with a 100fps high speed camera. Based on the LRCN model, we combine the MobileNets (V2) and LSTM for this task, the MobileNets (V2) for extracting the image features and recognize the gestures, then, Long Short-Term Memory (LSTM) architecture for interpreting the features across time steps. Around 100 samples are taken for each gesture for training at first, then, the samples are augmented to 200 samples per gesture by data augmentation. Result shows that the model is able to learn the gestures varying in duration and complexity and gestures can be recognized in 88ms with 90.62% accuracy in the experiment on our hand gesture dataset.
Decentralized Access Control Encryption in Public Blockchain

Since its invention, the public blockchain has attracted more attention from both the academia and industry because of its fully decentralization and persistency features. However, the privacy issue in public blockchain is still challengeable. While there exists privacy preservation mechanisms proposed for the public blockchain, almost all of them can only solve partial of the privacy issue, either user privacy or data privacy indeed, in it. In this work, we present a decentralized access control encryption scheme which ensures user and data privacy simultaneously in public blockchain. With our cryptographic solution, the validity of one specific transaction can be publicly verified, while its content can only be retrieved by its intended receivers. Moreover, the origin of this transaction cannot be identified by any participant except the receivers in the network. Our analysis shows that our solution is really suitable to deploy in public blockchain and is proven secure under mathematical assumptions.
Attention-Based Pose Sequence Machine for 3D Hand Pose Estimation

Most of the existing methods for 3D hand pose estimation are performed from a single depth map. In that case, the depth missing challenges from input frames caused by hand self-occlusions and imaging quality lead to multi-valued mapping phenomenon and sub-optimal model. In this paper, we proposed a novel recurrent architecture named Attention-based Pose Sequence Machine (APSM) to alleviate challenges by introducing temporal consistency. As for recurrent unit (RU), we extend traditional Gated Recurrent Unit (GRU) with 3D convolutional neural networks (CNNs) to handle voxelized inputs and features, and a novel RU named Deep Gated Recurrent Unit (DGRU) was proposed by rebuilding deeper gates based on GRU. To improve the model performance, a novel spatial attention mechanism denoted as Attention Model (AM) was proposed. Ablation experiments are designed to validate each contribution of our work, and experiments on two publicly available dataset show that our work outperforms state-of-the-art on hand pose estimation.