Reviewerly/exHarmony huggingface dataset:
An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain.
Adaptive Knowledge Driven Regularization for Deep Neural Networks

In many real-world applications, the amount of data available for training is often limited, and thus inductive bias and auxiliary knowledge are much needed for regularizing model training. One popular regularization method is to impose prior distribution assumptions on model parameters, and many recent works also attempt to regularize training by integrating external knowledge into specific neurons. However, existing regularization methods did not take account of the interaction between connected neuron pairs, which is invaluable internal knowledge for adaptive regularization for better representation learning as training progresses. In this paper, we explicitly take into account the interaction between connected neurons, and propose an adaptive internal knowledge driven regularization method, CORR-Reg. The key idea of CORR-Reg is to give a higher significance weight to connections of more correlated neuron pairs. The significance weights adaptively identify more important input neurons for each neuron. Instead of regularizing connection model parameters with a static strength such as weight decay, CORR-Reg imposes weaker regularization strength on more significant connections. As a consequence, neurons attend to more informative input features and thus learn more diversified and discriminative representation. We derive CORR-Reg with Bayesian inference framework and propose a novel optimization algorithm with Lagrange multiplier method and Stochastic Gradient Descent. Extensive evaluations on diverse benchmark datasets and neural network structures show that CORR-Reg achieves significant improvement over state-of-the-art regularization methods.
Blockchain Isn’t as Unbreakable as You Think

Sometimes it seems as if everyone has bought into the hype: Industries as far-flung as real estate and diamond sales have embraced block-chain without entirely knowing what it is or how its most vaunted features might fail or have unintended consequences. Block-chain assures users that once information has been stored, it can never be deleted or falsified. This means that when people in finance, say, pore over the history of a transaction, they feel content in the knowledge that illegalities have nowhere to hide. It means that people in the supply chain of a product trust that they can check its provenance without fear that misinformation has been slipped in along the way. In essence, block-chain promises not just complete data security but also something more intangible: that we will never be conned. Is it really so important that we understand what’s under the hood?
Comprehensive physics testing and adaptive weather research and forecasting physics for day‐ahead solar forecasting

Abstract Numerical weather prediction (NWP) models, which attempt to simulate the full state of the atmosphere, come with many options for dealing with processes that are unable to be explicitly resolved by the model. These model parameterizations are an ongoing area of research in atmospheric science. However, with continuous contributions from the research community and subsequent upgrade of the NWP codes, there are often many options for each unresolved process—leaving the user confronted with potentially thousands of ways to configure the model. We use the weather research and forecasting (WRF) model to forecast global horizontal irradiance (GHI) and undertake the task of narrowing down these options for a location in Qinghai, China. We show that optimizing the configuration of the WRF model based on the type of day (sunny, partly cloudy, or cloudy) is 13.6% better than using a single best configuration for all types of days. We also show that this performance improvement holds true for a longer 3‐month test period (17.8% improvement).
Quantumness of ensemble via coherence of Gram matrix

The Gram matrix of a set of vectors, which encapsulates the relations between the constituent vectors, plays an important role in the exploration of both geometric and information-theoretic aspects of quantum state space. In view of its usefulness and importance, we study the Gram matrix of an ensemble of pure states (a set of pure states with a prior probability distribution) and reveal its fundamental properties. We highlight and exploit the fact that the Gram matrix of an ensemble can be formally regarded as a bona fide quantum state. The key idea is to employ coherence (with respect to the computational basis) of the Gram matrix (regarded as a quantum state) to quantify quantumness of the corresponding ensemble. In particular, we propose to use the l 1 -norm of coherence and the relative entropy of coherence, of the Gram matrix, as two significant quantifiers of quantumness. We illustrate the effectiveness and power of these quantifiers of quantumness by evaluating them for several important ensembles arising from quantum measurement and quantum cryptography. We further compare the quantumness based on the Gram matrix with various other quantifiers of quantumness in the literature, and show that although they are closely related in general, they have subtle differences and capture different aspects of ensembles, which shed light on the complexity of quantum ensembles.
Dynamic Selective Edge-Based Integer/Fractional-Order Partial Differential Equation for Degraded Document Image Binarization

Conventional thresholding algorithms have had limited success with degraded document images. Recently, partial differential equations (PDEs) have been applied with good results. However, these are usually tailored to handle relatively few specific distortions. In this study, we combine an edge detection term with a linear binarization source term in a PDE formulation. Additionally, a new proposed diffusivity function further amplifies desired edges. It also suppresses undesired edges that comprise bleed-through effects. Furthermore, we develop the fractional variant of the proposed scheme, which further improves results and provides more flexibility. Moreover, nonlinear color spaces are utilized to improve binarization results for images with color distortion. The proposed scheme removes document image degradation such as bleed-through, stains, smudges, etc., and also restores faded text in the images. Experimental subjective and objective results show consistently superior performance of the proposed approach compared to the state-of-the-art PDE-based models.
Automation of Security and Privacy Controls for Efficient Information Security Management

Information security management incorporates both security and privacy controls where security program deals with the security of information and information systems to assure confidentiality, integrity, and availability and privacy programs handle agreement with appropriate confidentiality requirements and problems integrated with personally identifiable information. So, when it comes to the functioning and controlling of any aspect of an organization both controls should be properly implemented. However, the complexity and the scope of organizations with rapidly evolving technology risks make management difficult. To achieve efficiency in implementation and monitoring, controls are required to be managed automatically. It is shown in the paper that 47.8% from NIST Special Publication 800-53 revision 5 [3] recommended controls for high impact systems can be automated. To facilitate automation for rest 52.2% non-automatable controls, it is shown that implementation of compensating controls provides the same degree of security. Moreover, the paper includes examples of tools and applications that support automation of controls and examples of challenges in the automation process.
Beyond awareness

As cyber security challenges have multiplied across society, there is increasing confusion about how those challenges should be addressed. For many years, computer security was the preserve of a small cadre within the IT profession: in most cases, limited to a few individuals in banks and those concerned with national security. It now seems to be widely accepted that this narrow perspective is simplistic and inadequate, because it fails to address some of the biggest problems. And yet a vision of cyber security as “everyone’s problem” seems equally untenable. Many people are simply not equipped to make meaningful decisions relating to this topic, whether in their private or professional lives. In addressing contemporary cyber security challenges, a “responsibility paradox” has therefore emerged: while cyber security extends beyond the purview of IT departments, leadership may be lacking in addressing the broad topic in all its dimensions. This chapter explores the value of involving an eclectic range of disciplines, organisations, and skill-sets in addressing cyber security challenges. Acknowledging the impracticalities of everyone becoming a “cyber security expert”, this chapter discusses practical measures for overcoming skill gaps. There is much work to be done – in understanding the challenge, and building the inter-disciplinary contexts in which the necessary deep changes in thought and practice can take place.
Robust Feature Matching via Hierarchical Local Structure Visualization

Feature matching, which refers to seeking good correspondence between two feature point sets, is a critical prerequisite in many applications of remote sensing and photogrammetry. This work can be viewed as an extension of LSV-ANet. Traditional local structure visualization (LSV) descriptor is very sensitive to outliers existing in the small region around a feature point, which limits the ability of LSV-ANet to recognize fine-grained patterns and its generalizability for complex matching scenes. Thus, in this letter, we propose a hierarchical local structure visualization (HLSV) to solve this issue. Specifically, local structures of feature points are first decoupled by hierarchical tensor and then reconstructed in a learning fashion, which explicitly permits us to learn a nonmutually exclusive relationship for enhancement structure manipulation. In addition, in order to further improve the network generalization ability, we design a permutation-invariant network layer to ensure that the output of model is invariant to the input order. To demonstrate the robustness of the HLSV, extensive experiments on various real remote sensing image pairs for feature matching are conducted. The experiment results reveal that HLSV is superior to the current eight state-of-the-art alternatives.
A Reconfigurable Quantum Local Area Network Over Deployed Fiber

We demonstrate a three-node telecom quantum local area network over deployed fiber. It has eight independent entanglement channels which are dynamically reconfigurable. We successfully show entanglement demand balancing across the network and quantify its quality.
Practical considerations for using grating produced Bessel beams for alignment purposes

Bessel beams are useful for alignment because they create a small diameter, bright, straight line image in space perpendicular to the Axicon, or Axicon grating, producing the beam that is an exact analog of a single ray in a ray tracing program. Here we limit our discussion to Bessel beams produced by plane gratings whose pattern is evenly spaced concentric circles that are illuminated by a point source of light on the grating axis. The gratings produce a more nearly ideal Bessel beam than a lens type Axicon, and the plane grating serves as a plane mirror as well in an alignment setup so the combination define four degrees of freedom in space rather than the usual two. Most discussions of Bessel beams assume illumination with collimated light. We have found it advantageous to use a point source for illumination because it is easy and less expensive to use a single mode fiber as a source than a precision collimating lens the diameter of the Axicon. Besides, collimated illumination produces a Bessel beam of finite length in transmission while in theory a beam of infinite length is created using a point source. With these assumptions about how the beams are produced and details about the grating diameter and line spacing it is easy to calculate the useful length of the Bessel beam in reflection from the grating, the usual matter of concern when using the grating for alignment purposes in a double pass test setup. Other practical matters are also discussed such as lens centering with a test apparatus with no moving parts.
Blended Learning in English and English-medium Physics Classes Using Augmented Reality, Edmodo, and Tinkercad Media

Globalization and the emergence of IT also rendered English more relevant for second language learners. This study aims to determine the effectiveness of blended learning in improving student learning outcomes in English and physics, using augmented reality, Edmodo, and tinkercad media. This is a quasi-experimental study with data randomly obtained from 70 students of Public Senior High School (SMA) 2 on Lubuk Pakam, Indonesia. The samples were divided into two groups, each totaling 35 students. One of the classes is an experiment with blended learning, while the other is a control class comprising the conventional method. Furthermore, the research instrument consisted of a learning outcome test, in the form of an objective test administered during the pre-test and post-test in the form of an observation sheet. The effectiveness of blended learning in improving learning outcomes was analyzed using the independent sample t-test with SPSS 17. The results showed that the blended learning model effectively improves student learning outcomes through the independent and paired t-test samples of 0.148 and 0.000, significance differences, respectively. This study concludes that blended learning using augmented reality, Edmodo, and tinkercad media effectively improves student learning outcomes and can make them active
Blockchain-based Parametric Health Insurance

In traditional indemnity policy insurance, the payment is made to the insurer based on the actual loss or the damage from an insured physical asset. Therefore, the settlement is typically a very long process as there are multiple parties involved. In comparison, parametric insurance insures a policyholder against the occurrence of a specific event by paying a set amount based on the magnitude of the event.We have seen that with digitization, the insurance industries have also been disrupted through the use of innovative insurance schemes, one of them being parametric insurance. In parametric insurance, a fixed amount is paid on an event once a certain threshold is crossed. The amount is determined based on the modeled forecast of the loss.Parametric insurance will help settle the claim process, which currently is challenged with a longer settlement duration and disputes. However, for parametric insurance to work, there is a need for the data providers to capture events and their trust factor. In the new data-driven age, technologies like IoT, API's, and distributed ledgers are the technology enablers to capture real-time events and add a trust factor with the distributed ledger. Though parametric insurance can be implemented with the traditional centralized database approach, it has limitations on trust issues as the data can be manipulated.This paper explores how we can realize trusted digital technology-enabled parametric health insurance. It will deep dive into the technical solution, challenges to design, deploy, and parametric service insurance. A proof of concept was done for health-based parametric insurance, and the resultant blueprint, which can be applied for other use cases, is shared in sections ahead
Differentiable Allophone Graphs for Language-Universal Speech Recognition

Building language-universal speech recognition systems entails producing phonological units of spoken sound that can be shared across languages. While speech annotations at the language-specific phoneme or surface levels are readily available, annotations at a universal phone level are relatively rare and difficult to produce. In this work, we present a general framework to derive phone-level supervision from only phonemic transcriptions and phone-to-phoneme mappings with learnable weights represented using weighted finite-state transducers, which we call differentiable allophone graphs. By training multilingually, we build a universal phone-based speech recognition model with interpretable probabilistic phone-to-phoneme mappings for each language. These phone-based systems with learned allophone graphs can be used by linguists to document new languages, build phone-based lexicons that capture rich pronunciation variations, and re-evaluate the allophone mappings of seen language. We demonstrate the aforementioned benefits of our proposed framework with a system trained on 7 diverse languages.
A A Survey of the Link Prediction on Static and Temporal Knowledge Graph

Link prediction in knowledge graphs gradually plays an essential role in the field of research and application. Through detecting latent connections, we can refine the knowledge in the graph, discover interesting relationships, answer user questions or make item suggestions. In this paper, we conduct a survey of the methods that are currently achieving good results in link prediction. Specially, we perform surveys on both static and temporal graphs. First, we divide the algorithms into groups based on the characteristic representation of entities and relations. After that, we describe the original idea and analyze the key improvements. In each group, comparisons and investigation on the pros and cons of each method as well as their applications are made. Based on that, the correlation of the two graph types in link prediction is drawn. Finally, from the overview of the link prediction problem, we propose some directions to improve the models for future studies.
An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan

Background Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization. Methods After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection. Results As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform. Conclusions The methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients. Trial Registration ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041 International Registered Report Identifier (IRRID) DERR1-10.2196/30083