Reviewerly/exHarmony huggingface dataset:
Blockchain in Healthcare: A Panacea or Scourge

Blockchain technology is finding its way into every industry and healthcare is no exception. By decentralising patient health history, tracking pharmaceuticals and improving payment options, blockchain holds the promise of remodeling the collaborative exchange of vital research and useful healthcare data. At the same time, there are still significant obstacles to the broader use of blockchain. Among other things, its novel and distinct characteristics, particularly its consensus-driven decision making and decentralised authority, are somewhat at odds with the clearly delineated framework of rights and obligations. Therefore, for any blockchain project today, it is crucial to understand the application of existing laws in relation to blockchain, especially in the context of data protection, allocation of liability and determination of governing law. This article describes key characteristics of blockchain technology, analyses possible use cases in healthcare, and delves into legal issues and questions that arise in relation to these blockchain applications.
Swachh AI: Real-time Spit Detection using YOLOv3 Darknet Model

In today’s time, all the government authorities are fighting day and night to stop the spread of Coronavirus. Hence, the aim of the system is to focus on using the latest computer technologies to limit the spread of communicable diseases like the Coronavirus by imposing a strict restriction on public spitting, using real-time drone-based surveillance systems, which is responsible for the maximum replication of the virus. To implement this system, CCTV/drone will capture video feed from public places and record live activities of that area. This data will then be sent via Cloud and analyzed using the YOLOv3 Darknet model to detect public spitting. The results will be available to the local authorities using a web/mobile interface. The officials can then penalize the individual. Thus, this system will assist the officials to manage and monitor public spitting in an efficient way.
The changes in miR-221 and miR-222 before and after interventional therapy of coronary heart disease and analysis of their correlation with inflammatory factors and prognosis.

OBJECTIVE To explore the changes in miR-221 and miR-222 before and after interventional therapy of coronary heart disease and their relationship with inflammatory factors and prognosis. METHODS A total of 122 subjects with coronary heart disease who underwent interventional therapy in our hospital from January 2017 to January 2019 were chosen as the observation group, and 122 healthy people during the same period were chosen as the control group. We retrospectively analyzed the levels of serum miR-221, miR-222, C-reactive protein (CRP), tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6). Pearson correlation analysis was used to reveal the relationship between serum levels of miR-221, miR-222 and CRP, TNF-α and IL-6, N-terminal B-type brain natriuretic peptide precursor (NT-proBNP) and left ventricular ejection fraction (LVEF%) in the observation group. The levels of serum miR-221, miR-222, TNF-α, CRP and IL-6 before and after treatment were compared in the observation group. After a follow-up of 6 months, the observation group was divided into a poor-prognosis group (26 cases) and a good-prognosis group (96 cases) according to whether there was an adverse cardiovascular event or not. The levels of serum miR-221 and miR-222 before and after intervention treatment were compared between the two groups. And the clinical values of miR-221 and miR-222 levels before and after intervention treatment in the observation group were analyzed by the ROC curve. RESULTS The levels of serum miR-221, miR-222, CRP, TNF-α, and IL-6 in the observation group were markedly higher than those of the control group. And levels of serum miR-221, miR-222 were negatively correlated with LVEF% while positively correlated with CRP, TNF-α, IL-6 and NT-proBNP (P 0.800). CONCLUSION The levels of miR-221 and miR-222 in patients with coronary heart disease significantly increased and they were closely correlated with the inflammatory factors, NT-proBNP and LVEF%. The levels of miR-221 and miR-222 before and after treatment have certain clinical value in evaluating the prognosis of patients.
Entropy transfer from a quantum particle to a classical coherent light field

In the field of light-matter interactions, it is often assumed that a classical light field that interacts with a quantum particle remains almost unchanged and thus contains nearly no information about the manipulated particles. To investigate the validity of this assumption, we develop and theoretically analyze a simple Gedankenexperiment which involves the interaction of a coherent state with a quantum particle in an optical cavity. We quantify the resulting alteration of the light field by measuring the fidelity of its initial and equilibrium states. Using Bayesian inference, we demonstrate the information transfer through photon measurements. In addition, we employ the concepts of quantum entropy and mutual information to quantify the entropy transfer from the particle to the light field. In the weak coupling limit, we validate the usually assumed negligible alteration of the light field and entropy transfer. In the strong coupling limit, however, we observe that the information of the initial particle state can be fully encoded in the light field, even for large photon numbers. Nevertheless, we show that spontaneous emission is a sufficient mechanism for removing the entropy initially stored in the particle. Our analysis provides a deeper understanding of the entropy exchange between quantum matter and classical light.
Research and Simulation of UAV Three-Dimensional Path Replanning in Complex Environment

In order to make the research of unmanned aerial vehicle (UAV) path replanning closer to the actual combat environment, a path replanning algorithm for UAV based on model-based predictive control and particle swarm optimization is proposed to study the three-dimensional (3D) path replanning problem in situations involving multiple kinds of threats. Firstly, the interactive model of constant velocity (CV) model and coordinated turn (CT) model is used to describe the motion state of the sudden threat, and the Kalman filter method is used to predict the state of the sudden threat, considering the systematic error in the movement process of the sudden threat and the detection error of the airborne radar. Then, based on the 3D dynamics model of UAV, a 3D path planning model is established, and the path replanning problem is transformed into a series of optimization sub-problems by using the idea of model-based predictive control (MPC). Finally, the sub-problem is solved by using particle swarm optimization (PSO) algorithm. The experimental results show that the trajectory prediction of motion sudden threat in this paper is more accurate, and the designed algorithm not only meets the validity and real-time requirements of path replanning, but also has strong robustness.
Towards Service Ontology for Web Services Storage and Discovery

The ontology has marked its presence in several research fields in order to address issues in knowledge modeling' Artificial Intelligence, classification, clustering and more specifically in knowledge engineering. In the Web Service field, ontology is used principally to describe the Web Service interface for discovering, storing and composing Web services. This is well known as semantic Web services, such as OWL-S, WSMO and WSDL-S. Unfortunately, work in this field remain insufficient till now; so, supporting this description by adding a shared ontology (knowledge base) can improve the performance of the Web Service technology. Manual construction of ontology is a tedious and expensive task which requires domain expert intervention; an automatic ontological construction process will be interesting and less costly solution in terms of time and money. In this paper, we propose to use clustering algorithms to create an ontological network (Service Ontology (SO)) from a semantic Web Services corpus. The created SO can be used for storage and discovery of Web Services in a distributed and intelligent environment.
Analysis of multimedia application and research based on web pages

Multimedia technology is the fastest growing and most influential technology in the field of modern information technology. It is widely used in many industries such as network, education, books, military, consulting services, communications, finance, and medical treatment.With the development of the information age, the construction and development of web sites based on the Internet has developed rapidly with the development of network information technology. The construction of e-commerce websites has gradually developed into a platform for enterprises and institutions to communicate with the outside world.
Exploration and Analysis on “Online + Offline” Blended Teaching Mode Reform

The “online+offline” blended teaching mode integrates the conventional offline teaching and online teaching, fully arouses students' enthusiasm for autonomous learning. The blended teaching mode is deeply studied from the perspective of the type, features and advantages of blended teaching, its relationship with conventional teaching and implementation procedures, etc. The various developed teaching resources are used to build the teaching research special platform, and the “online+offline” blended teaching mode is established on the basis of offline class teaching and class online autonomous learning and outward development so as to make students fully master theoretical knowledge and make in-depth consideration and remarkably promote learning effects. Meanwhile, this teaching mode also imposes higher requirements on the quantity and quality of online teaching resources and teachers' proficiency of information-based teaching instrument.
Influence of computer technologies popularization on modern youth today and tomorrow

Educational process of the past is different from the system which is being implemented at the present time. The reason for this is the change of the set of requirements to the range of skills and competences that future employees should possess. The article compares two generation groups, Generation Z (‘Others’) and Generation Y (‘ Millenials’). It explains what difficulties they might have while communicating together. The paper gives grounding for hypertextual perception of reality and ‘nomophobia’ that are characteristic features of Generation Z. The article explains why the youth are responsive on gamification and mind mapping technologies; what problems they have with time management and reading skills and why it is important to implement hands-on technologies into the teaching process.
Multi-Order Adversarial Representation Learning for Composed Query Image Retrieval

This paper targets at a task of composed query image retrieval. Given a composed query consists of a reference image and modification text, the task aims to retrieve images which are generally similar to the reference image but differ according to the given modification text. The task is challenging, due to the complexity of the composed query and cross-modality characteristics between the query and candidate images. The common paradigm for the task is to first obtain fused feature of the reference image and the text, and further project them into a common embedding space with candidate images. However, the majority of works usually only aim for the representation of high level, ignoring the low-level representation which may be complementary to the high-level representation. So this paper proposes a new Multi-order Adversarial Network (MAN) which uses multilevel representations and simultaneously explores their low-order and high-order interactions, obtaining low-order and high-order features. The low-order features reflect the pattern of itself and high-order features contains the interaction between features. Moreover, we further introduce an adversarial module to constrain the fusion of the reference image and the text. Extensive experiments on three datasets verify the effectiveness of our MAN and also demonstrate its state-of-the-art performance.
Enhancing Deep Semantic Segmentation of RGB-D Data with Entangled Forests

Semantic segmentation is a problem which is getting more and more attention in the computer vision community. Nowadays, deep learning methods represent the state of the art to solve this problem, and the trend is to use deeper networks to get higher performance. The drawback with such models is a higher computational cost, which makes it difficult to integrate them on mobile robot platforms. In this work we want to explore how to obtain lighter deep learning models without compromising performance. To do so we will consider the features used in the 3D Entangled Forests algorithm and we will study the best strategies to integrate these within FuseNet deep network. Such new features allow us to shrink the network size without loosing performance, obtaining hence a lighter model which achieves state-of-the-art performance on the semantic segmentation task and represents an interesting alternative for mobile robotics applications, where computational power and energy are limited.
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor Embedding

The dimensionality reduction has been widely introduced to use the high-dimensional data for regression, classification, feature analysis, and visualization. As the one technique of dimensionality reduction, a stochastic neighbor embedding (SNE) was introduced. The SNE leads powerful results to visualize high-dimensional data by considering the similarity between the local Gaussian distributions of high and low-dimensional space. To improve the SNE, a t-distributed stochastic neighbor embedding (t-SNE) was also introduced. To visualize high-dimensional data, the t-SNE leads to more powerful and flexible visualization on 2 or 3-dimensional mapping than the SNE by using a t-distribution as the distribution of low-dimensional data. Recently, Uniform manifold approximation and projection (UMAP) is proposed as a dimensionality reduction technique. We present a novel technique called a q-Gaussian distributed stochastic neighbor embedding (q-SNE). The q-SNE leads to more powerful and flexible visualization on 2 or 3-dimensional mapping than the t-SNE and the SNE by using a q-Gaussian distribution as the distribution of low-dimensional data. The q-Gaussian distribution includes the Gaussian distribution and the t-distribution as the special cases with q=1.0 and q=2.0. Therefore, the q-SNE can also express the t-SNE and the SNE by changing the parameter q, and this makes it possible to find the best visualization by choosing the parameter q. We show the performance of q-SNE as visualization on 2-dimensional mapping and classification by k-Nearest Neighbors (k-NN) classifier in embedded space compared with SNE, t-SNE, and UMAP by using the datasets MNIST, COIL-20, OlivettiFaces, FashionMNIST, and Glove.
Compositional Embedding Models for Speaker Identification and Diarization with Simultaneous Speech From 2+ Speakers

We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedding methods such as x-vector [2], compositional embedding models contain a function f that separates speech from different speakers. In addition, they include a composition function g to compute set-union operations in the embedding space so as to infer the set of speakers within the input audio. In an experiment on multi-person speaker identification using synthesized LibriSpeech data, the proposed method outperforms traditional embedding methods that are only trained to separate single speakers (not speaker sets). In a speaker diarization experiment on the AMI Headset Mix corpus, we achieve state-of-the-art accuracy (DER=22.93%), slightly better than the previous best result (23.82% from [3]).
Efficient Relational Sentence Ordering Network

In this paper, we propose a novel deep Efficient Relational Sentence Ordering Network (referred to as ERSON) by leveraging pre-trained language model in both encoder and decoder architectures to strengthen the coherence modeling of the entire model. Specifically, we first introduce a divide-and-fuse BERT (referred to as DF-BERT), a new refactor of BERT network, where lower layers in the improved model encode each sentence in the paragraph independently, which are shared by different sentence pairs, and the higher layers learn the cross-attention between sentence pairs jointly. It enables us to capture the semantic concepts and contextual information between the sentences of the paragraph, while significantly reducing the runtime and memory consumption without sacrificing the model performance. Besides, a Relational Pointer Decoder (referred to as RPD) is developed, which utilizes the pre-trained Next Sentence Prediction (NSP) task of BERT to capture the useful relative ordering information between sentences to enhance the order predictions. In addition, a variety of knowledge distillation based losses are added as auxiliary supervision to further improve the ordering performance. The extensive evaluations on Sentence Ordering, Order Discrimination, and Multi-Document Summarization tasks show the superiority of ERSON to the state-of-the-art ordering methods.
Error Bounds for Some Approximate Posterior Measures in Bayesian Inference

In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational cost of doing so. This problem motivates the use of approximate posteriors that arise from approximating the data misfit or forward model. We review some error bounds for random and deterministic approximate posteriors that arise when the approximate data misfits and approximate forward models are random.
A human-in-the-loop framework to handle implicit bias in crowdsourced KGs

Crowd-sourced Knowledge Graphs (KGs) may be biased: some biases can originate from factual errors, while others reflect different points of view. How to identify and measure biases in crowd-sourced KGs? And then, how to tell apart factual errors from different point of views? And how to put together all these steps contextualized in a human-in-the-loop framework?
Smart Contract and Blockchain Based Contract Management System

This paper presents theoretical and practical research on the possibilities of applying smart contracts in the field of law and a contract management system which allows users to conclude contracts based on blockchain technology. The transition from traditional contracting to smart contracting would significantly improve the contracting process as contracts concluded in this way would be without intermediaries. Therefore, they could be concluded more efficiently and the costs related to the contracts could be reduced. Allotment contract was selected as an example, i.e., a contract by which the hotel representative assigns certain accommodation capacities to a travel agency and by which the agency provides those capacities in their arrangements. A smart allotment contract has been written using the Solidity programming language, to run on the Ethereum platform.
On the properties of generalized cyclotomic binary sequences of period $$2p^m$$

Xiao, Zeng, Li and Helleseth proposed new generalized cyclotomic binary sequences $$s^{\infty }$$ of period $$p^m$$ and showed that these sequences are almost balanced and have very large linear complexity if p is a non-Wieferich prime and $$m=2$$ . Wu, Xu, Chen and Ke determined the values of the k-error linear complexity for $$m=2$$ in terms of the theory of Fermat quotients and the results indicated that sequences $$s^{\infty }$$ have good stability. Edemskiy, Li, Zeng and Helleseth studied the linear complexity of $$s^{\infty }$$ for general integers $$m\ge 2$$ . Furthermore, Ouyang and Xie constructed new $$2p^{m}$$ -periodic binary sequences $${\widehat{s}}^{\infty }$$ and $${\widetilde{s}}^{\infty }$$ and proved that the sequences $${\widehat{s}}^{\infty }$$ and $${\widetilde{s}}^{\infty }$$ are of high linear complexity when $$m\ge 2$$ . In this paper we shall show that despite a high linear complexity the sequences $$s^{\infty }$$ , $${\widehat{s}}^{\infty }$$ and $${\widetilde{s}}^{\infty }$$ have some undesirable features which may not suggest them for cryptography. The properties of multiplicative character sums modulo $$p^m$$ play an important role in the proof of this paper.
Identifying Emerging Trends in Scientific Texts Using TF-IDF Algorithm: A Case Study of Medical Librarianship and Information Articles

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential.&#x0D; Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information.&#x0D; Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms.&#x0D; Results: The results obtained from the TF-IDF algorithm indicated that the words “Library”, “Patient”, and “Inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book”, and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore”, and “Intervent” were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “Telerehabilit”, “Instagram”, “WeChat”, and “Comic” were new keywords observed in articles on medical librarianship and information between 2015 and 2020.&#x0D; Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.
Apurinã Universal Dependencies Treebank

This paper presents and discusses the first Universal Dependencies treebank for the Apurinã language.The treebank contains 76 fully annotated sentences, applies 14 parts-of-speech, as well as seven augmented or new features -some of which are unique to Apurinã.The construction of the treebank has also served as an opportunity to develop finite-state description of the language and facilitate the transfer of open-source infrastructure possibilities to an endangered language of the Amazon.The source materials used in the initial treebank represent fieldwork practices where not all tokens of all sentences are equally annotated.For this reason, establishing regular annotation practices for the entire Apurinã treebank is an ongoing project.