Reviewerly/exHarmony huggingface dataset:
A Self-Sovereign Identity Management System Using Blockchain

Blockchain is emerging as a functional technology for remodeling current technologies and also for creating new applications which in practical was not possible earlier. In this paper, we are building a self-sovereign identity management system using blockchain (BSelSovID). The application will serve as an interface to all the entities involved, viz. user, authority, and verifier. The use of blockchain is to dodge the centralized identity manager. The user gets digital identity after getting authority’s verifiable claim and the identity is stored on interplanetary file system (IPFS) and the content address of these is stored on the blockchain. This makes it easy for the user to interact with different services without having identity for each one of them. The security of user data is ensured by encrypting the data and giving user the complete control over his data stored in the IPFS. The blockchain cannot extract the user’s data but can only request the user to provide some data attribute or some verification as the verifier demands. Thus, the system provides security and privacy to users’ data as well as user has control over his data.
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.
Rail Topology Ontology: A Rail Infrastructure Base Ontology

Engineering projects for railway infrastructure typically involve many subsystems which need consistent views of the planned and built infrastructure and its underlying topology. Consistency is typically ensured by exchanging and verifying data between tools using XML-based data formats and UML-based object-oriented models. A tighter alignment of these data representations via a common topology model could decrease the development effort of railway infrastructure engineering tools. A common semantic model is also a prerequisite for the successful adoption of railway knowledge graphs. Based on the RailTopoModel standard, we developed the Rail Topology Ontology as a model to represent core features of railway infrastructures in a standard-compliant manner. This paper describes the ontology and its development method, and discusses its suitability for integrating data of railway engineering systems and other sources in a knowledge graph. With the Rail Topology Ontology, software engineers and knowledge scientists have a standard-based ontology for representing railway topologies to integrate disconnected data sources. We use the Rail Topology Ontology for our rail knowledge graph and plan to extend it by rail infrastructure ontologies derived from existing data exchange standards, since many such standards use the same base model as the presented ontology, viz., RailTopoModel.
A BPM-based framework for the impact assessment of blockchain to the midstream LNG supply chain

Purpose The current study addresses how blockchain can deal with the challenges that the midstream liquefied natural gas (LNG) supply chain poses combined from a management standpoint. Such challenges are: the volume of transactions, communication hurdles and the lack of contemporary management tools. The paper proposes a comprehensive framework to assess the impact of blockchain implementation in the midstream LNG supply chain in order to tackle those barriers. Design/methodology/approach The basis of the research is the business process modelling (BPM), through which entities, roles, tasks, resources and transactions can be modelled and simulated. The modelling of the midstream LNG supply chain, via BPM, is based on guidelines of the Society of International Gas Tanker and Terminal Operators (SIGGTO) and common industry business models. A quantitative analysis is employed to support the motivation and the potential impact of blockchain implementation. The methodology is used to identify (1) inefficiencies related to large volume of transactions between stakeholders and (2) critical areas of an LNG shipping company, where blockchain can be implemented. Findings Process repeatability, numerous shared documentation forms, excessive paperwork and communication imbroglios are mapped from the modelling section. Up to 327 processes are repeated during a typical vessel voyage, and up to 122 shared documentation forms are exchanged. Excessive paperwork and communication imbroglios are tracked through, which correspond to 25 severe errors as detected. By implementing the methodology, stakeholders can quantify the possible impact of blockchain on the operational performance of each stakeholder's operations separately and the supply chain as a whole in terms of real-time monitoring, transparency and paperwork reduction, time and cost savings. Research limitations/implications The research has certain limitations deriving from its conceptual nature. The business processes' modelling is based on standard procedures described in the guidelines by SIGGTO and may need further adjustment for specific use cases. A structured case study has not been realisable as corporate data for an LNG shipping company regarding processes and other commercial sensitive information are required. Practical implications Potential practitioners may exploit the proposed framework as a low cost and seamless tool to evaluate how blockchain could disrupt their operations. Thus, the blockchain implementation's improvements or weaknesses can be pinpointed, and enabling the interested stakeholder of the LNG supply chain with specific feedback, it can guide them towards informed decisions on their operations. Originality/value The research has a novel approach as it combines the creation of practical management framework, with a comprehensive visualization of the midstream LNG supply chain. Thus, the reader can identify in which parts of the midstream LNG supply chain can blockchain be implemented, and what impact it could have in terms of supply chain operations.
Saliency area detection algorithm of electronic information and image processing based on multi-sensor data fusion

Abstract Researched in the 1980s, multi-sensor data convergence has become a hot issue. Not only does it differ from general signal processing, or single to multiple sensor surveillance and measurement, on the other hand, it is a higher level of integrated decision-making processes based on multiple sensor measurement outcomes, this paper is based on the study of the saliency area detection algorithm of electronic information and image processing based on multi-sensor data fusion, based on the improved FT algorithm and LC algorithm using multi-sensor data fusion technology, a new LVA algorithm is proposed, and these three algorithms are evaluated in an all-round way through various algorithm evaluation indicators such as PR curve, PRF histogram, MAE index, and recognition image rate. The research results show that the LVA algorithm proposed in this paper improves the detection rate of saliency maps by 5–10%.
Enlightenment of Physical Education Teaching Experiment Based on Cloud Computing to the Current Physical Education Reform

Our country has a large land area, and the development of physical education is not balanced. In daily teaching activities, teachers and students use computers and networks to teach, which generates massive amounts of data. Schools are limited by funds and cannot meet the growing demand for storage of teaching resources. It is also unable to realize the sharing of teaching resources. In order to solve the problems existing in the existing education and teaching platform of the school, especially in the teaching reform, to meet the requirements of all parties facing physical education, the concept of cloud computing was proposed, and the services and methods provided by the cloud computing-based teaching resource platform were discussed. Through the questionnaire survey method of college students and teachers, statistical methods and logical analysis methods were used to analyze the data collected in the questionnaire. Summary and analysis are as follows. The survey results show that more than 50% of the people are dissatisfied with the current physical education and believe that it has not played its due role, and more than 70% of the people agree with the reform of physical education. The experimental results also show that interesting and diverse physical education courses can attract students to participate and increase their interest. From the overall survey results, the problem of college physical education courses is more serious, and it is urgent to optimize teaching from the cloud computing level. On the one hand, it is necessary to improve the relevant cloud computing and other technical platform facilities; on the other hand, it is necessary to improve the teaching level of teachers and change the current educational concept to make it livelier and more interesting.
Enforcing Sets of Formulas in Structured Argumentation

Enforcement, adjusting an argumentation framework such that a certain set of arguments becomes acceptable, is an important research topic within the study of dynamic argumentation, but one that has been little studied for structured argumentation. In this paper we study enforcement in a general structured argumentation setting. In particular, we study conditions on the argumentation setting and the knowledge base that ensure (or prevent) the acceptability of sets of formulas for structured argumentation frameworks.
Characteristics of patients in platform C19, a COVID-19 research database combining primary care electronic health record and patient reported information

Background Data to better understand and manage the COVID-19 pandemic is urgently needed. However, there are gaps in information stored within even the best routinely-collected electronic health records (EHR) including test results, remote consultations for suspected COVID-19, shielding, physical activity, mental health, and undiagnosed or untested COVID-19 patients. Observational and Pragmatic Research Institute (OPRI) Singapore and Optimum Patient Care (OPC) UK established Platform C19, a research database combining EHR data and bespoke patient questionnaire. We describe the demographics, clinical characteristics, patient behavior, and impact of the COVID-19 pandemic using data within Platform C19. Methods EHR data from Platform C19 were extracted from 14 practices across UK participating in the OPC COVID-19 Quality Improvement program on a continuous, monthly basis. Starting 7 th August 2020, consenting patients aged 18–85 years were invited in waves to fill an online questionnaire. Descriptive statistics were summarized using all data available up to 22 nd January 2021. Findings From 129,978 invitees, 31,033 responded. Respondents were predominantly female (59.6%), white (93.5%), and current or ex-smokers (52.6%). Testing for COVID-19 was received by 23.8% of respondents, of which 7.9% received positive results. COVID-19 symptoms lasted ≥4 weeks in 19.5% of COVID-19 positive respondents. Up to 39% respondents reported a negative impact on questions regarding their mental health. Most (67%-76%) respondents with asthma, Chronic Obstructive Pulmonary Disease (COPD), diabetes, heart, or kidney disease reported no change in the condition of their diseases. Interpretation Platform C19 will enable research on key questions relating to COVID-19 pandemic not possible using EHR data alone.
Bayesian tests of symmetry for the generalized Von Mises distribution

Abstract Bayesian tests on the symmetry of the generalized von Mises model for planar directions (Gatto and Jammalamadaka in Stat Methodol 4(3):341–353, 2007) are introduced. The generalized von Mises distribution is a flexible model that can be axially symmetric or asymmetric, unimodal or bimodal. A characterization of axial symmetry is provided and taken as null hypothesis for one of the proposed Bayesian tests. The Bayesian tests are obtained by the technique of probability perturbation. The prior probability measure is perturbed so to give a positive prior probability to the null hypothesis, which would be null otherwise. This allows for the derivation of simple computational formulae for the Bayes factors. Numerical results reveal that, whenever the simulation scheme of the samples supports the null hypothesis, the null posterior probabilities appear systematically larger than their prior counterpart.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a huge impact on both industrial and scientific applications. However, community efforts to advance large-scale graph ML have been severely limited by the lack of a suitable public benchmark. For KDD Cup 2021, we present OGB Large-Scale Challenge (OGB-LSC), a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. Furthermore, OGB-LSC provides dedicated baseline experiments, scaling up expressive graph ML models to the massive datasets. We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale. Our datasets and baseline code are released and maintained as part of our OGB initiative (Hu et al., 2020). We hope OGB-LSC at KDD Cup 2021 can empower the community to discover innovative solutions for large-scale graph ML.
Well-Classified Examples Are Underestimated in Classification with Deep Neural Networks

The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary. For instance, when training with cross-entropy loss, examples with higher likelihoods (i.e., well-classified examples) contribute smaller gradients in back-propagation. However, we theoretically show that this common practice hinders representation learning, energy optimization, and margin growth. To counteract this deficiency, we propose to reward well-classified examples with additive bonuses to revive their contribution to the learning process. This counterexample theoretically addresses these three issues. We empirically support this claim by directly verifying the theoretical results or significant performance improvement with our counterexample on diverse tasks, including image classification, graph classification, and machine translation. Furthermore, this paper shows that we can deal with complex scenarios, such as imbalanced classification, OOD detection, and applications under adversarial attacks because our idea can solve these three issues. Code is available at https://github.com/lancopku/well-classified-examples-are-underestimated.
Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking

As the environment of the Internet of Things (IoT) gradually becomes common and mature, various smart application (APP) platforms have sprung up, making what we are doing more convenient, more economical and more efficient. Then, this paper used a bank APP as the research background to discuss issues related to smart APPs. Obviously, through the bank APPs, customers can complete their transfer and payment for various expenses at home, eliminating the inconvenience of going out, which not only can alleviate traffic congestion as well as reduce carbon emissions but also can save the manpower expenditure costs for banks. Consequently, improving APP performance and increasing the number of users of an APP is a very important issue. Therefore, this paper proposed an APP performance index to evaluate the performance of a bank APP. This APP performance index is to evaluate the performance of the APP through the time interval of customers’ access to the APP. The shorter the time interval is, the greater the number of users within a unit time is. In addition, based on cost considerations and effectiveness, the sample size n is usually not too large in practice, in order to make decisions quickly and accurately in a short time. Since the fuzzy testing model based on the confidence interval can be integrated with the past accumulated experience of data experts, the testing accuracy can be leveled up under the condition of small-sized samples. Accordingly, a fuzzy evaluation model was proposed to evaluate whether the performance of the bank APP can reach the required level, and this model was also regarded as a basis for decision-making to determine whether to improve the bank APP. At the same time, we can grasp the opportunities for improvement, achieve the effect of cost reduction, energy saving and carbon reduction, and further move towards the goal of innovative and intelligent management.
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction

Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the historical events in the script.Two kinds of information facilitate this task, namely, the event-level information and the script-level information.At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants.At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist.In this paper, we propose a Transformer-based model, called M-CPredictor, which integrates deep event-level and script-level information for script event prediction.At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations.At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event.The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
Information-Corrected Estimation: A Generalization Error Reducing Parameter Estimation Method

Modern computational models in supervised machine learning are often highly parameterized universal approximators. As such, the value of the parameters is unimportant, and only the out of sample performance is considered. On the other hand much of the literature on model estimation assumes that the parameters themselves have intrinsic value, and thus is concerned with bias and variance of parameter estimates, which may not have any simple relationship to out of sample model performance. Therefore, within supervised machine learning, heavy use is made of ridge regression (i.e., L2 regularization), which requires the the estimation of hyperparameters and can be rendered ineffective by certain model parameterizations. We introduce an objective function which we refer to as Information-Corrected Estimation (ICE) that reduces KL divergence based generalization error for supervised machine learning. ICE attempts to directly maximize a corrected likelihood function as an estimator of the KL divergence. Such an approach is proven, theoretically, to be effective for a wide class of models, with only mild regularity restrictions. Under finite sample sizes, this corrected estimation procedure is shown experimentally to lead to significant reduction in generalization error compared to maximum likelihood estimation and L2 regularization.
A Deep Hybrid Approach for Hate Speech Analysis

Hate speech is about making insults or stereotypes towards a person or a group of people based on its characteristics such as origin, race, gender, religion, and more. Thus, hate speech can be classified using machine learning and deep learning methods, and it gives a distinguished output from one class to another. Also, every day tons of data are getting accumulated from social media. However, the single deep learning model cannot provide the diversified feature for text classification due to data characteristics. Therefore, this paper proposes two methods for hate speech classification. Initially, a majority voting classifier with three deep learning hybrid models is presented. Finally, a multi-channel convolutional neural network with a bi-directional gated recurrent unit and capsule network is introduced. The proposed approach helps in improving the classification accuracy and ground truth information by reducing ambiguity. The proposed models are verified using six different data sets. The experimental outcomes demonstrate that the proposed methods achieve adequate results for hate speech classification.
Improved Superconducting Qubit State Readout by Path Interference

High fidelity single shot qubit state readout is essential for many quantum information processing protocols. In superconducting quantum circuit, the qubit state is usually determined by detecting the dispersive frequency shift of a microwave cavity from either transmission or reflection. In this paper, we demonstrate the use of constructive interference between the transmitted and reflected signal to optimize the qubit state readout, with which we find a better resolved state discrimination and an improved qubit readout fidelity. As a simple and convenient approach, our scheme can be combined with other qubit readout methods based on the discrimination of cavity photon states to further improve the qubit state readout.
TETRA™ Techniques to Assess and Manage the Software Technical Debt

The paper examines the company's proprietary means for determining the quality of a software product and measuring its technical debt.The paper's authors explain how a software product's quality is directly correlated with the amount of varying technical debts that the end-users receive.All debts can be paid, and technical debt is no exception: one can use various parameters, techniques, and dimensions to effectively measure and optimize the quality of a software product.The authors share information about the company's proprietary method to technical debt management, which is done via the Technical dEbT Reduction plAtform, otherwise known as TETRA™.They give details about the assessment's major dimensions, tools, and measurement parameters.