Reviewerly/exHarmony huggingface dataset:
A Novel and Effective Image Super-Resolution Reconstruction Technique via Fast Global and Local Residual Learning Model

The principle of image super-resolution reconstruction (SR) is to pass one or more low-resolution (LR) images through information processing technology to obtain the final high-resolution (HR) image. Convolutional neural networks (CNN) have achieved better results than traditional methods in the process of an image super-resolution reconstruction. However, if the number of neural network layers is increased blindly, it will cause a significant increase in the amount of calculation, increase the difficulty of training the network, and cause the loss of image details. Therefore, in this paper, we use a novel and effective image super-resolution reconstruction technique via fast global and local residual learning model (FGRLR). The principle is to directly train a low-resolution small image on a neural network without enlarging it. This will effectively reduce the amount of calculation. In addition, the stacked local residual block (LRB) structure is used for non-linear mapping, which can effectively overcome the problem of image degradation. After extracting features, use 1 × 1 convolution to perform dimensional compression, and expand the dimensions after non-linear mapping, which can reduce the calculation amount of the model. In the reconstruction layer, deconvolution is used to enlarge the image to the required size. This also reduces the number of parameters. We use skip connections to use low-resolution information for reconstructing high-resolution images. Experimental results show that the algorithm can effectively shorten the running time without affecting the quality of image restoration.
Image Denoising with Overlapping Group Sparsity and Second Order Total Variation Regularization

We propose an image denoising method by combining overlapping group sparsity and second-order total variation regularization. The method is named OGS-SOTV (image denoising method based on Overlapping Group Sparsity and Second-Order Total Variation regularization). The method utilizes performance of noise removal of overlapping group sparsity and performance of artifacts elimination of second-order total variation. A regularization parameter estimation is also proposed to implement the method automatically. In experiments, we compare denoising results of OGS-SOTV with ones of other similar methods. Results confirmed that OGS-SOTV works effectively and outperforms other similar denoising methods.
GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user's finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods.
Cross-modal dual subspace learning with adversarial network

Cross-modal retrieval has recently attracted much interest along with the rapid development of multimodal data, and effectively utilizing the complementary relationship of different modal data and eliminating the heterogeneous gap as much as possible are the two key challenges. In this paper, we present a novel network model termed cross-modal Dual Subspace learning with Adversarial Network (DSAN). The main contributions are as follows: (1) Dual subspaces (visual subspace and textual subspace) are proposed, which can better mine the underlying structure information of different modalities as well as modality-specific information. (2) An improved quadruplet loss is proposed, which takes into account the relative distance and absolute distance between positive and negative samples, together with the introduction of the idea of hard sample mining. (3) Intra-modal constrained loss is proposed to maximize the distance of the most similar cross-modal negative samples and their corresponding cross-modal positive samples. In particular, feature preserving and modality classification act as two antagonists. DSAN tries to narrow the heterogeneous gap between different modalities, and distinguish the original modality of random samples in dual subspaces. Comprehensive experimental results demonstrate that, DSAN significantly outperforms 9 state-of-the-art methods on four cross-modal datasets.
Development of Quantum Private Queries Protocol on Collective-Dephasing Noise Channel

Quantum private queries can commonly protect important information in a good many of domains, such as finance, business, military, which use quantum effects to achieve unprecedented classical private queries. However, quantum state can be easily affected by environmental noise, which affects the actual effect of quantum private queries. This paper developed a new quantum private query protocol based on four qubits logical Bell state to resist the collective-dephasing noise. The symmetric private information retrieval problem, which is the most influential problem in the process of quantum private query, was solved well by quantum oblivious transfer. It introduces the construction of four qubits logical Bell state. The quantum private query protocol innovates the quantum key distribution process by using the four qubits logical Bell state as the measurement base to measure the logical qubits, and ensures the function of quantum oblivious transmission. The protocol cannot only resist the noise influence of the communication process, but also ensure the security of both sides of the communication.
Modeling cryptocurrencies volatility using GARCH models: a comparison based on Normal and Student's T-Error distribution

This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1,1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards.The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student's t distribution.The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns.Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.
Phase compensation for free-space continuous-variable quantum key distribution

Large-scale and flexible deployment of quantum networks is possible with reliable free-space quantum key distribution. However, signal fading occurs in free-space channels and causes various adverse effects. Under this circumstance, phase compensation becomes a challenging task for quantum key distribution using continuous variables. Here we investigate the feasibility of implementing phase compensation via simply computing the correlation between transmitted and received data. Demonstration and performance analysis are conducted with real transmittance of a 150-m free-space fading channel; results indicate the applicability of this compensation scheme to free-space quantum communication systems.
Inverse-system method for identification of damping-rate functions in non-Markovian quantum systems

Identification of complicated quantum environments lies in the core of quantum engineering, which systematically constructs an environment model with the aim of accurate control of quantum systems. In this paper, we present an inverse-system method to identify damping rate functions, which describe non-Markovian environments in time-convolution-less master equations. To access information on the environment, we couple a finite-level quantum system to the environment and measure time traces of local observables of the system. By using sufficient measurement results, an algorithm is designed, which can simultaneously estimate multiple damping rate functions for different dissipative channels. Further, we show that identifiability for the damping rate functions corresponds to the invertibility of the system and a necessary condition for identifiability is also given. The effectiveness of our method is shown in examples of an atom and three-spin-chain non-Markovian systems.
News Image Captioning Based on Text Summarization Using Image as Query

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions contain more detailed information such as entity names and events than generic image captions do. Detailed information is usually contained in news text but not in news images. Generic image captioning does not make full use of the accompanying news text to generate image captions. This paper proposes a news image captioning method based on the attentional encoder-decoder model through summarizing the news text according to query image. The multi-modal attentional mechanism is proposed to compute the context vector. The proposed model is trained on the DailyMail news image captioning corpora which are created by collecting images, caption, news texts through parsing the html-formatted documents. Experiments on the DailyMail test dataset show that the proposed method outperforms the generic image captioning and the text summarization method.
Multiplex Memory Network for Collaborative Filtering

Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services.Although current deep neural network-based collaborative filtering methods have achieved state-of-the-art performance in recommender systems, they still face a few major weaknesses.Most importantly, such deep methods usually focus on the direct interaction between users and items only, without explicitly modeling high-order co-occurrence contexts.Furthermore, they treat the observed data uniformly, without fine-grained differentiation of importance or relevance in the user-item interactions and high-order co-occurrence contexts.Inspired by recent progress in memory networks, we propose a novel multiplex memory network for collaborative filtering (MMCF).More specifically, MMCF leverages a multiplex memory layer consisting of an interaction memory and two co-occurrence context memories simultaneously, in order to jointly capture and locate important and relevant information in both user-item interactions and co-occurrence contexts.Lastly, we conduct extensive experiments on four datasets, and the results show the superior performance of our model in comparison with a suite of state-of-the-art methods.
The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis

Aim: To evaluate the value of S-Detect (a computer aided diagnosis system using deep learning) in breast ultrasound (US) for discriminating benign and malignant breast masses.Material and methods: A literature search was performed and relevant studies using S-Detect for the differential diagnosis of breast masses were selected. The quality of included studies was assessed using a Quality Assessment of Diagnostic Accuracy Studies (QUADAS) questionnaire. Two review authors independently searched the articles and assessed the eligibility of the reports.Results: A total of ten studies were included in the meta-analysis. The pooled estimates of sensitivity and specificity were 0.82 (95%CI: 0.77-0.87) and 0.86 (95%CI: 0.76-0.92), respectively. In addition, the diagnostic odds ratios, positive likelihood ratio and negative likelihood ratio were 28 (95%CI: 16- 49), 5.7 (95%CI: 3.4-9.5), and 0.21 (95%CI: 0.16-0.27), respectively. Area under the curve was 0.89 (95%CI: 0.86-0.92). No significant publication bias was observed.Conclusions: S-Detect exhibited a favourable diagnostic value in assisting physicians discriminating benign and malignant breast masses and it can be considered as a useful complement for conventional US.
Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems

In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems.Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm.Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability.Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms.Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.
Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial

Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health research­ers on the application of machine learning methods to conduct precision medicine research designed to reduce health dispari­ties. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advan­tages and disadvantages of different learning approaches, describe strategies for interpret­ing “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.Ethn Dis. 2020;30(Suppl 1):217-228; doi:10.18865/ed.30.S1.217
Improved Deep Multi-Patch Hierarchical Network With Nested Module for Dynamic Scene Deblurring

Dynamic scene deblurring is a significant technique in the field of computer vision. The multi-scale strategy has been successfully extended to the deep end-to-end learning-based deblurring task. Its expensive computation gives birth to the multi-patch framework. The success of the multi-patch framework benefits from the local residual information passed across the hierarchy. One problem is that the finest levels rarely contribute to their residuals so that the contributions of the finest levels to their residuals are excluded by coarser levels, which limits the deblurring performance. To this end, we substitute the nested module blocks, whose powerful and complex representation ability is utilized to improve the deblurring performance, for the building blocks of the encoder-decoders in the multi-patch network. Additionally, the attention mechanism is introduced to enable the network to differentiate blur across the whole blurry image from dynamic scene, thereby further improving the ability to handle the motion object blur. Our modification boosts the contributions of the finest levels to their residuals and enables the network to learn different weights for feature information extracted from spatially-varying blur image. Extensive experiments show that the improved network achieves competitive performance on the GoPro dataset according to PSNR and SSIM.
Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference

For billions of people living in remote and rural communities in the developing countries, small water systems are the only source of clean drinking water. Due to the rural nature of such water systems, site visits may occur infrequently. This means broken water systems can remain in a malfunctioning state for months, forcing communities to return to drinking unsafe water. In this work, we present a novel two-level anomaly detection system aimed to detect malfunctioning remote sensored water hand-pumps, allowing for a proactive approach to pump maintenance. To detect anomalies, we need a model of normal water usage behavior first. We train a multilevel probabilistic model of normal usage using approximate variational Bayesian inference to obtain a conditional probability distribution over the hourly water usage data. We then use this conditional distribution to construct a level-1 scoring function for each hourly water observation and a level-2 scoring function for each pump. Probabilistic models and Bayesian inference collectively were chosen for their ability to capture the high temporal variability in the water usage data at the individual pump level as well as their ability to estimate interpretable model parameters. Experimental results in this work have demonstrated that the pump scoring function is able to detect malfunctioning sensors as well as a change in water usage behavior allowing for a more responsive and proactive pump system maintenance.
Re-ranking image-text matching by adaptive metric fusion

Image-text matching has drawn much attention recently with the rapid growth of multi-modal data. Many effective approaches have been proposed to solve this challenging problem, but limited effort has been devoted to re-ranking methods. Compared with the uni-modal re-ranking methods, modality heterogeneity is the major difficulty when designing a re-ranking method in the cross-modal field, which mainly lies in two aspects of different visual and textual feature spaces and different distributions in inverse directions. In this paper, we propose a heuristic re-ranking method called Adaptive Metric Fusion (AMF) for image-text matching. The method can obtain a better metric by adaptively fusing metrics based on two modules: 1) Cross-modal Reciprocal Encoding, which considers ranks in inverse directions to comprehensively evaluate a metric. The sentence retrieval and image retrieval have different distribution characteristics and galleries in different modalities, thus it is necessary to exploit them simultaneously for appropriate metric fusion. 2) Query Replacement Gap, which quantifies the gap between cross-modal and uni-modal similarities to alleviate the influence of different visual and textual feature spaces on the fused metric. The proposed re-ranking method can be implemented in an unsupervised way without requiring any human interaction or annotated data, and can be easily applied to any initial ranking result. Extensive experiments and analysis validate the effectiveness of our method on the large-scale MS-COCO and Flickr30K datasets.
RoLMA: A Practical Adversarial Attack Against Deep Learning-Based LPR Systems

With the advances of deep learning, license plate recognition (LPR) based on deep learning has been widely used in public transport such as electronic toll collection, car parking management and law enforcement. Deep neural networks are proverbially vulnerable to crafted adversarial examples, which has been proved in many applications like object recognition, malware detection, etc. However, it is more challenging to launch a practical adversarial attack against LPR systems as any covering or scrawling to license plate is prohibited by law. On the other hand, the created perturbations are susceptible to the surrounding environment including illumination conditions, shooting distances and angles of LPR systems. To this end, we propose the first practical adversarial attack, named as RoLMA, against deep learning-based LPR systems. We adopt illumination technologies to create a number of light spots as noises on the license plate, and design targeted and non-targeted strategies to find out the optimal adversarial example against HyperLPR, a state-of-the-art LPR system. We physicalize these perturbations on a real license plate by virtue of generated adversarial examples. Extensive experiments demonstrate that RoLMA can effectively deceive HyperLPR with an 89.15% success rate in targeted attacks and 97.3% in non-targeted attacks. Moreover, our experiments also prove its high practicality with a 91.43% success rate towards physical license plates, and imperceptibility with around 93.56% of investigated participants being able to correctly recognize license plates.
Pandawan Website Design Based Content Management System As Media E-commerce Transaction

As the Regulation of the Minister of Research, Technology and Higher Education of the Republic of Indonesia No. 9 of 2018 concerning the accreditation of scientific journals article 4 which mentions the requirements of scientific journals, it has been announced on the official website page of ARJUNA (National Journal Accreditation), that regarding the Circular of the Director of Intellectual Property Management Number 101 / E5.2 / SE / 2018 dated 18 January 2018 regarding Accreditation of Scientific Periodicals only accepts and processes the proposed national scientific (periodical) journal accreditation that has been managed electronically, so that the assessment process will be easier, faster, accurate and transparent (ARJUNA, 2018). But in reality in the era of the industrial revolution 4.0, the Government was not well organized. There are a total of 3 (three) problems from 2 (two) sides of website users, namely there are still many institutions or agencies that in the management use conventional processes so that people who want to publish their articles digitally are constrained with limited access, there are still minimal agencies that have not been able to provide a forum to provide educational support facilities and Open Journal System (OJS) in the form of attractive website-based e-commerce. In this study there are 2 (two) data collection methods, namely observation and literature study methods. The results of this study are Pandawan e-commerce based websites that provide journal publisher services that can be managed online, as well as solving publisher problems in managing journals online.