Reviewerly/exHarmony huggingface dataset:
HTR-Flor: A Deep Learning System for Offline Handwritten Text Recognition

In recent years, Handwritten Text Recognition (HTR) has captured a lot of attention among the researchers of the computer vision community. Current state-of-the-art approaches for offline HTR are based on Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, deep models such as CRNNs, Recur-rent Neural Networks (RNNs) are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. Besides, they usually have millions of parameters which require huge amount of data, and computational resource. Recently, a new class of neural net-work architecture, called Gated Convolutional Neural Networks (Gated-CNN), has demonstrated potentials to complement CRNN methods in modeling. Therefore, in this paper, we present a new architecture for HTR, based on Gated-CNN, with fewer parameters and fewer layers, which is able to outperform the current state-of-the-art architectures for HTR. The experiment validates that the proposed model has statistically significant recognition results, surpassing previous HTR systems by an average of 33% over five important handwritten benchmark datasets. Moreover, the proposed model is able to achieve satisfactory recognition rates even in case of few training data. Finally, its compact architecture requires less computational resources, which can be applied for real-world applications that have hardware limitations, such as robots and smartphones.
Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review

Background Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. Objective This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. Methods The Arksey and O’Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. Results The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. Conclusions We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID) PRR1-10.2196/22505
Design thinking as an approach for innovation in healthcare: systematic review and research avenues

Design thinking has been increasingly adopted as an approach to support innovation in healthcare. Recent publications report design thinking application to various innovation projects, across medical specialties, including paediatrics, psychiatry, radiology, gastroenterology, oncology, orthopaedics and surgery, as well as to innovation in hospital operations and healthcare management. Current literature in the area typically focuses on single case descriptions. With the recent increase in the number of cases, there is an opportunity to assess multiple cases to identify patterns and avenues for further research. This study provides a systematic review of published design thinking projects in healthcare. The aim of the study is to provide an overview of how design thinking has been applied in the healthcare sector. Data collection was based on Institute of Scientific Information (ISI) Web of Science, PubMed and Scopus databases. The systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 32 original pieces of research was selected for analysis, being classified and assessed. The paper presents current status of research and practice from various perspectives, including the design thinking progression phase—inspiration, ideation, implementation—and the prevalence of design thinking tools. Avenues for further research include the need to increase focus on the inspiration phase, the opportunity for platforms for leveraging the integration of individuals in innovation projects, and the opportunity to enhance the role of lead users in healthcare innovation.
Regulating smart contracts: Legal revolution or simply evolution?

Blockchain-based smart contracts have triggered polarised discussions. They have been applauded as a significant technological achievement, but also criticised as a dumb idea. Their application is rapidly expanding in the financial sector, public sector, supply chain management, and the automobile, real estate, insurance, and health care industries. With the growing use of smart contracts and an increasing variety of smart contracts applications, the debate over the legal implications of this phenomenon has intensified and many legal issues related to smart contracts are being examined. Legal scholars have highlighted potential legal pitfalls, controversies and incompatibilities with existing legal frameworks. Blockchain technology and smart contracts have also been fuelling an interest of legislators, who have begun to recognise regulatory uncertainties and are making the first attempts to introduce legislative solutions to address them. This paper aims to highlight the fervour of the scholarly debate surrounding smart contracts and contrast it with a rather modest response from the legislators thus far. The paper reiterates that smart contracts represent the future. Even though they challenge practitioners, scholars, and legislators, current legislative initiatives indicate that under most legal systems there are no major obstacles for smart contracts and to accommodate smart contracts within the existing legal frameworks we should expect legal evolution rather than revolution.
Joint Feature Synthesis and Embedding: Adversarial Cross-Modal Retrieval Revisited

Recently, generative adversarial network (GAN) has shown its strong ability on modeling data distribution via adversarial learning. Cross-modal GAN, which attempts to utilize the power of GAN to model the cross-modal joint distribution and to learn compatible cross-modal features, is becoming the research hotspot. However, the existing cross-modal GAN approaches typically 1) require labeled multimodal data of massive labor cost to establish cross-modal correlation; 2) utilize the vanilla GAN model that results in unstable training procedure and meaningless synthetic features; and 3) lack of extensibility for retrieving cross-modal data of new classes. In this article, we revisit the adversarial learning in existing cross-modal GAN methods and propose Joint Feature Synthesis and Embedding (JFSE), a novel method that jointly performs multimodal feature synthesis and common embedding space learning to overcome the above three shortcomings. Specifically, JFSE deploys two coupled conditional Wassertein GAN modules for the input data of two modalities, to synthesize meaningful and correlated multimodal features under the guidance of the word embeddings of class labels. Moreover, three advanced distribution alignment schemes with advanced cycle-consistency constraints are proposed to preserve the semantic compatibility and enable the knowledge transfer in the common embedding space for both the true and synthetic cross-modal features. All these add-ons in JFSE not only help to learn more effective common embedding space that effectively captures the cross-modal correlation but also facilitate to transfer knowledge to multimodal data of new classes. Extensive experiments are conducted on four widely used cross-modal datasets, and the comparisons with more than ten state-of-the-art approaches show that our JFSE method achieves remarkably accuracy improvement on both standard retrieval and the newly explored zero-shot and generalized zero-shot retrieval tasks.
284.8-Mb/s Physical-Layer Cryptographic Key Generation and Distribution in Fiber Networks

Key distribution is a major challenge of secure communication. Computational approaches to key distribution are breached by quantum algorithms, while quantum key distribution (QKD) is an expensive, slow, and sophisticated solution. Secure key generation and distribution from unpredictable and inherent physical-layer properties of optical fiber channel is a classical, simple and cost-effective solution. However, the quasi-static nature of fiber channels dramatically limits the available key generation rate (KGR) to the order of ~kbit/s. Here we effectively accelerate the KGR by six orders of magnitude using a developed high-speed chaotic polarization scrambler (CPS) driven by digital chaos in fiber channels. An error-free KGR of 284.8 Mbit/s is experimentally demonstrated over a 24 km standard single-mode fiber (SSMF), where the generated key passes the random test suite. Moreover, we fully analyze the security mechanism and find that a strong asymmetry exists between legal and illegal users, ensuring a high-level of security against potential fiber-tapping attacks. This scheme provides a major step towards the practical implementation of the “one-time-pad” in secure data transmission over fiber networks.
Human Motion Prediction Using Adaptable Recurrent Neural Networks and Inverse Kinematics

Human motion prediction, especially arm prediction, is critical to facilitate safe and efficient human-robot collaboration (HRC). This letter proposes a novel human motion prediction framework that combines a recurrent neural network (RNN) and inverse kinematics (IK) to predict human arm motion. A modified Kalman filter (MKF) is applied to adapt the model online. The proposed framework is tested on collected human motion data with up to 2 s prediction horizon. The experiments demonstrate that the proposed method improves the prediction accuracy by approximately 14% comparing to the state-of-art on seen situations. It stably adapts to unseen situations by keeping the maximum prediction error under 4 cm, which is 70% lower than other methods. Moreover, it is robust when the arm is partially occluded. The wrist prediction remains the same, while the elbow prediction has 20% less variation.
A Data Preprocessing Method for Automatic Modulation Classification Based on CNN

As a backbone of deep learning models, convolutional neural networks (CNNs) are widely used in the field of automatic modulation classification. Nevertheless, we speculate that the forms of signal samples make them inefficient for direct use as a CNN input. In this letter, a novel data preprocessing method is proposed to markedly improve CNN-based automatic modulation classification. The benchmark dataset used in this research is the well-known RadioML2016.10a dataset. The experimental results show that using the proposed method gains approximately 10% accuracy improvement in a simple CNN. Furthermore, according to the form of the preprocessed data, we designed a CNN with residual blocks to reach a maximum accuracy of 93.7% when the signal-to-noise ratio is 14 dB, which outperforms state-of-the-art automatic modulation classifiers.
Bayesian network based label correlation analysis for multi-label classifier chain

Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible for predicting the relevance of one label. When training the classifier for a label, proceeding labels will be taken as extended features. If the extended features are highly correlated to the label, the performance will be improved, otherwise, the performance will not be influenced or even degraded. How to discover label correlation and determine the label order is critical for CC approach. This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC). Conditional entropy is used to describe the dependency relations among labels, and a BN is built up by taking nodes as labels and weights of edges as their dependency relations. A new scoring function is proposed to evaluate a BN structure, and a heuristic algorithm is introduced to optimize the BN. At last, by applying topological sorting on the nodes of the optimized BN, the label order for constructing CC model is derived. Experiments demonstrate the feasibility and effectiveness of the proposed method.
State Estimation Methods in Navigation: Overview and Application

This article deals with state estimation of nonlinear stochastic dynamic systems. The stress is laid on general introduction of the selected estimation methods, description of their application in navigation systems, and treatment of the error sources in the navigation systems design.
Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization

High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access to and adoption of this technique. As a potential solution to these challenges, we present a deep learning-based approach making use of single image super-resolution (SISR) to reconstruct high-resolution histology images from low-resolution inputs. Such low-resolution images can easily be shared, require less storage, and can be acquired quickly using widely available low-cost slide scanners. The network consists of multi-scale fully convolutional networks capable of capturing hierarchical features. Conditional generative adversarial loss is incorporated to penalize blurriness in the output images. The network is trained using a progressive strategy where the scaling factor is sampled from a normal distribution with an increasing mean. The results are evaluated with quantitative metrics and are used in a clinical histopathology diagnosis procedure which shows that the SISR framework can be used to reconstruct high-resolution images with clinical level quality. We further propose a self-supervised color normalization method that can remove staining variation artifacts. Quantitative evaluations show that the SISR framework can generalize well on unseen data collected from other patient tissue cohorts by incorporating the color normalization method.
A Prediction based Encryption Approach for Telemedicine Applications

Abstract The rapid progression of health care technologies systems and transmission strategies makes it reliable to gain, dispense and manage data over medical devices and as well improves conventional hospital information systems (HIS) to deliver effective health care services. When the medical information is communicated through wireless network, there exists a high chance of modifying the information. Before examining the patient, the physician has to check for the integrity of received medical image. A futuristic tele healthcare framework has been proposed to ensure the security, for offering complete healthcare services at reduced cost. In this paper, the proposed framework encompasses the integration of three modules viz. Prediction, Padding and Chaotic map encryption. This work offers an enhanced security for both patient medical data as well as medical scan images to a great extent. Two processes are involved in the padding sequence namely: forward and backward snail tour. After the snail tour process, the adjacent pair of pixels in the original image is XORing in order to make it ready for further process using the chaotic map encryption algorithm. Three operations have been carried out in chaotic approach namely: permutation, diffusion and substitution. This encryption method arises, a valid confusion and diffusion in the image pixels such that, the security of the images/data is enhanced. The performance of the proposed approach is assessed and compared with existing schemes such as ECC and Chaotic map encryption on a set of MRI/CT medical images. Experimental outcomes demonstrate that the proposed framework offers robustness in terms of security, quality and reliability which alleviate misdiagnosis at the physician end in telemedicine applications.
Verifying the Safety of Autonomous Systems with Neural Network Controllers

This article addresses the problem of verifying the safety of autonomous systems with neural network (NN) controllers. We focus on NNs with sigmoid/tanh activations and use the fact that the sigmoid/tanh is the solution to a quadratic differential equation. This allows us to convert the NN into an equivalent hybrid system and cast the problem as a hybrid system verification problem, which can be solved by existing tools. Furthermore, we improve the scalability of the proposed method by approximating the sigmoid with a Taylor series with worst-case error bounds. Finally, we provide an evaluation over four benchmarks, including comparisons with alternative approaches based on mixed integer linear programming as well as on star sets.
MRI Image Segmentation Using Bat Optimization Algorithm with Fuzzy C Means (BOA-FCM) Clustering

Functional and anatomical information extraction from Magnetic Resonance Images (MRI) is important in medical image applications. The information extraction is highly influenced by the artifacts in the MRI images. The feature extraction involves the segmentation of MRI images. We present a MRI image segmentation using Bat Optimization Algorithm (BOA) with Fuzzy C Means (FCM) clustering. Echolocation of bats is utilized in Bat Optimization Algorithm. The proposed segmentation technique is evaluated with existing segmentation techniques. Results of experimentation shows that proposed segmentation technique outperforms existing methods and produces 98.5% better results.
In Blockchain We Trust: Does Blockchain Itself Generate Trust?

Objectives This study considers the affordance of users' perceived technological property of blockchains and investigates how users discover the possible actions that can be performed within blockchain media. With a focus on the role of trust, it analyzes how motivational affordances in blockchain media influence user experience Methods Qualitative methods are used to obtain the depth of understanding and elicit the perspective of blockchain media. We then conduct a survey to analyze the affordance of trust in the acceptance of blockchain media Results Our confirmed model indicates a heuristic dimension of trust regarding underlying ties to affective and technological affordances. These findings imply that cognitive heuristics affect users' decision making about privacy and security on blockchain media. These heuristics lead users to engage in uncertain and even risky transactions in blockchain media. Conclusions Despite the exponential growth in blockchain development, there has been little attention paid on how technological innovations of blockchains can produce value in the media sector. Our findings provide a lens to understand the blockchain's usability problems by pairing heuristics to blockchain design and user experience principles.
The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis

Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in detail, the results from a previously published algorithm automating the generation of tumor:stroma ratio (TSR) in colorectal clinical trial datasets. One dataset consisting of 2,211 cases and 106,268 expert-labelled images is used to identify quality issues, by visually inspecting cases where algorithm-pathologist agreement is lowest. Twelve categories are identified and used to analyze pathologist-algorithm agreement in relation to these categories. Of the 2,211 cases, 701 were found to be free from any image quality issues. Algorithm performance was then assessed, comparing pathologist agreement with image quality classification. It was found that agreement was lowest on poorly differentiated tissue, with a mean TSR difference of 0.25 (sd = 0.24). Removing images that contained quality issues increased accuracy from 80% to 83%, at the expense of reducing the dataset to 33,736 images (32%). Training the algorithm on the optimized dataset, prior to testing on all images saw a decrease in accuracy of 4%, indicating that the optimized dataset did not contain enough variation to generate a fully representative model. The results provide an in-depth perspective on image quality, highlighting the importance of the effects on downstream image analysis.
Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

The field artificial intelligence (AI) has been founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed though various stages of popularity and received recently a revival in the form of deep neural networks. Some problems of AI are that so far neither 'intelligence' nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to uncurtain the veil of vagueness surrounding AI to see its true countenance.