Reviewerly/exHarmony huggingface dataset:
CRQA: Credibility Retrieval for Medical Question Answer Service

With the rapid development of artificial intelligence and its wide application on the ground, more and more devices is embedded in intelligent question answering technology. The development of mobile Internet technology and the popularization of community platform provide us with huge and jumbled data. Credibility has become an urgent problem to be solved in question answering system. As far as the medical platform is concerned, there is a huge noise problem in the current general medical information retrieval. Based on this, we propose a trusted evaluation mechanism based on the information of the medical community platform. In order to search and select more accurate and credible answers, we propose an optimization method of search ranking based on credible evaluation mechanism. By analyzing the professional competence of information providers, this method trustfully ranks multiple answers in the question-and-answer set of medical communities, which largely solves the interference of “one-question multi-answer” phenomenon in the question-and-answer system on retrieval. At the same time, it uses department classification, subdivides medical directions, and further matches the areas of concern of information providers, so as to improve the accuracy and hit rate of the retrieval system.
Estimation of Sparsely Observed Signals with an Empirical Bayesian Model

In many applied settings, the statistical goal is to estimate underlying functions or signals from discrete observations contaminated with noise. The task is often prone to issues during data collection resulting in incomplete measurements (e.g., signals observed on a sparse set of time points, or with entire segments of the signal missing). To address this problem, we develop a Bayesian model that allows joint estimation of the amplitude (y-axis) and phase (x-axis) components of unknown signals. The major advantages of our method are the ability to (1) separately model these two sources of variability, (2) use smooth, complete data to incorporate prior knowledge about the underlying structure in a principled way, and (3) compute and quantify uncertainty of estimated signals. We build our model using a convenient square-root velocity function representation of signals, which allows for metric-based separation and statistical analysis of amplitude and phase. We validate the proposed framework using two simulation studies, and a real data application to estimation of fractional anisotropy profiles based on diffusion tensor imaging measurements on patients with multiple sclerosis.
Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries

Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.
Construction of Environment Art Design System in Web Mode

Abstract With the national emphasis on environmental issues is gradually increasing, more and more experts and scholars on the environmental art design system of more and more research. The requirement of environmental art design system is getting higher and higher. Traditionally, two-dimensional images are used to describe environmental art geography, and the environmental information obtained lacks intuitiveness and interactivity. The purpose of this paper is to realize the social sharing and service of environmental art information so that the public can conveniently enjoy the risk warning service of environmental information. Starting from the framework reconstruction of system construction, this paper takes the Web framework pattern as the basic framework of system realization, and creates different entity models and terrain models in virtual environment through Creator modeling software. After collecting the original environmental data, the edge processing of the environmental data is carried out to produce the 3D environmental design system. In the process of 3D virtual reproduction of the environment, Vega driver software was used to shape the Web 3D scene of the environment, and the Marine environment module in Vega was used to realize dynamic and static environment simulation and simulation effect. The experimental results show that the display effect of the environmental art design system constructed in the Web mode is more realistic, intuitive, interactive and satisfactory.
English as a We-Code: form and Function of English in Facebook Status Updates of Non-Native Female and Male Users

The English language has featured markedly as a popular language of computer-mediated communication, and notably of Facebook posts, written not only by native or second language speakers, but also users of English as a foreign language. The aim of this paper is to investigate the frequency, form and function of English language Facebook profile updates of 110 (55 women and 55 men) users of English representing 41 European, Asian, African and Latin American countries belonging to the Expanding Circle. Approached from the point of view of the code choice as well as the users’ gender, and supported by an online survey data, the study analyses in detail the form of the updates in connection with gender preferences and identifies language contexts and functions users choose to express themselves in English as opposed to their native tongue, thereby demonstrating the role of English as a we-code in a social networking service.
Mining Health Discussions on Suomi24

This paper discusses an effective way to fuse multiple tools to make a modern and easy-to-use software to get some key findings about health related topics on online social platforms, with a special focus on the famous Finnish forum, Suomi24. Several natural language processing have been tested and, sometimes, modified in order to accommodate the Finnish language linguistic structure and achieve our tasks of mining the health discussion content. We also explore the ability to monitor and track diseases in Finish open discussion forum. The developed modular system can help clinicians and medical experts to analyze similar forums to identify and track related events that can be correlated with hospital dataset in order to generate new hypotheses that initiate future treatment based approaches.
Analysis of Human Vital Functions Based on Data from Internet of Things Devices

The analysis of human vital functions is popular subject to discuss which is related to many fields of studies. One of the basic issue in this area is the development and implementing software for sensor devices with determined precision which collect data describing functions of human organs. The paper presents the use of IoT devices which collect and supply medial information for gathering and processing vital functions data. The collecting data system which processes information about sleep, heart functionality and physical activity from mobile devices has been created.
Implementation of Multi-channel/Multi-point Monitoring System Based on ATSC 3.0 Transmission Method

This paper is concerned with the implementation of a monitoring system for multi-channel/multi-point broadcasting signals transmitted through a terrestrial UHD Headend system based on ATSC 3.0. Implemented monitoring system measures the quality of jitter and delay of stream transmitted from the Headend, monitors additional services such as ESG and subtitles, and performs remote web monitoring function through various smart devices.
Conclusion: Mobility, Data and Learner Agency in Networked Learning

The aim of this chapter is to draw out insights concerning the central themes of the book, i.e. the themes of mobility, data and learner agency in networked learning. In the first section, we present an overview of the book’s chapters. We point out how the book’s three sections and the chapters that comprise them deal with one (or more) of the themes. This serves to highlight different contemporary takes of the networked learning community on the themes. We further highlight that the book’s ‘intro’ and ‘outro’ chapters pinpoint networked learning research practice. Between them, the chapters thus provide a characterization of the field of networked learning today, as seen through the lens of the book’s three themes. In the second section of the conclusion, we identify a set of issues emerging out of the community’s work with these themes: Demarcation and characterisation of the field of networked learning, the socio-material turn and evolving forms of networked learning design and assessment.
The Ethical and Legal Dimensions of Global Discipline of Cyberspace through the Harmonization of International Treaties

Background and Aim: Cyber space is changing rapidly. With the emergence of severe crises in this space, the harmonization of international treaties ethically can guarantee the creation of a global cyber dicipline. This study explores the ethical and legal dimensions of the global dicipline of cyberspace through the harmonization of international treaties. Materials and Methods: In this analytical study, based on international documents, the ethical and legal aspects of the globalization of international treaties are discussed and explained. Findings: Collaboration and participation in cyberspace globally requires the legal support of countries. There is also a coordinated role for governments to interact more by joining international treaties and integrating them to discipline of cyberspace. In fact, the globalization of cyberspace, as a new world, has transcended the limits of traditional laws. Therefore, domestic criminal regulations do not have the ability to deal with cybercrimes, and there is a growing need for countries to cooperate and interact with one another through the harmonization of international treaties and conformity with national laws based on ethical principles. Regardless of racial, regional, religious affiliations, respect for human dignity, human rights and non-discrimination; states must adhere to not only legally but also in terms of ethical values and norms, required to comply with international treaties. Conclusion: The global dicipline of cyberspace through the harmonization of treaties, calls for common ethical norms and universal rights and forged a common identity to build a united world community by transcending geographical boundaries and diversity of nations. Please cite this article as: Ahmadi Dastjerdi S, Sheikholeslami A, Hojabrosadati H. The Ethical and Legal Dimensions of Global Discipline of Cyberspace through the Harmonization of International Treaties. Bioethics Journal 2019; 9(33): 89-98.
Application of Artificial Intelligence Technology in Mechanical and Electronic Pump System

Abstract With the rapid development of modern science and technology today, a variety of emerging technologies representing advanced productivity can not only greatly improve the effect of equipment automation, but also promote the overall quality and level of manufacturing equipment, and even to the greatest extent to avoid the occurrence of safety accidents caused by human factors. Recently, in the mechatronic pump industry has emerged with the development of artificial intelligence technology color, the complexity of its internal structure and the precision of electronic control system has even reached a jaw-dropping degree. Artificial intelligence technology involves a wide range of knowledge, and has a very high degree of complexity and technology, the use of mechanical and electronic industry will certainly promote the whole industry to a higher stage of development. It can be expected that if ai technology can be applied continuously and stably in the mech industry, then the integrated drive system is expected to achieve the goal of high turnover and low cost, and will achieve value compensation in terms of economy and efficiency by improving operation characteristics and saving energy.
Tạo các câu hỏi trắc nghiệm toán tương đương nhau dựa trên Sagetex

The paper shows how to generate equivalent multiple-choice math questions automatically. Firstly, a general multiple-choice question, containing a set of parameters, is generated. Then, different values are assigned to these parameters to create equivalent multiple-choice questions. Parameter assignment is performed via the Sagetex, an instruction package of Latex. This package allows embedding Sagemath, a computer algebra software, into Latex, a document preparation system. Creating equivalent multiple-choice questions will help evaluate learners more accurately and fairly.
Supervised Human-Guided Data Exploration

Abstract An exploratory data analysis system should be aware of what a user already knows and what the user wants to know of the data. Otherwise it is impossible to provide the user with truly informative and useful views of the data. In our recently introduced framework for human-guided data exploration (Puolamäki et al. [20]), both the user’s knowledge and objectives are modelled as distributions over data, parametrised by tile constraints. This makes it possible to show the users the most informative views given their current knowledge and objectives. Often the data, however, comes with a class label and the user is interested only of the features informative related to the class. In non-interactive settings there exist dimensionality reduction methods, such as supervised PCA (Barshan et al. [1]), to make such visualisations, but no such method takes the user’s knowledge or objectives into account. Here, we formulate an information criterion for supervised human-guided data exploration to find the most informative views about the class structure of the data by taking both the user’s current knowledge and objectives into account. We study experimentally the scalability of our method for interactive use, and stability with respect to the size of the class of interest. We show that our method gives understandable and useful results when analysing real-world datasets, and a comparison to SPCA demonstrates the effect of the user’s background knowledge. The implementation will be released as an open source software library.
True scores for tartarus with adaptive GAs that evolve FSMs on GPU

The Tartarus Problem is one of the candidate benchmark problems in evolutionary algorithms. We take advantage of the graphical processing unit (GPU) to improve the results of the software agents that use finite state machines (FSMs) for this benchmark. While doing so we also contribute to the study of the problem on several grounds. Similar to existing studies we use genetic algorithms to evolve FSMs, but unlike most of them we use adaptive operators for controlling the parameters of the algorithm. We show that the actual number of valid boards is not 297,040, but 74,760, because the agent is indifferent to the rotations of the board. We also show that the agent can only come across 383 different combinations, rather than 6561 that is used in the current literature. A final contribution is that we report the first true scores for the agents by testing them with all available 74,760 boards. Our best solution has a mean score of 8.5379 on all boards.
Fair Scheduling for Deadline-Driven, Resource-Constrained, Multi-Analytics Workloads

In this paper, we analyze and empirically evaluate Justice, a fair-share, deadline-aware job scheduler for resource-constrained cloud deployments managed by big data resource negotiators. Justice provides admission control, which leverages historical traces and job deadlines to guide and adapt resource allocation decisions to changing cloud conditions. We evaluate Justice using different deadline types and production workloads. We find that it outperforms extant allocators in terms of fair allocation, deadline satisfaction, and useful work.
An Innovative Image-Based Tabular Data Extraction Parallel Algorithm

We propose a novel algorithm for extracting data from images of tabular documents having a specific structure. Our proposed method is able to maintain the original table format and structure, and offers better efficiency over existing methodologies due to its high scalability and parallel architecture. The findings of this paper will increase efficiency in the data extraction process from image-based tabular data and help ease the digitization process of tabular records.
High Throughput and Fully Pipelined FPGA Implementation of AES-192 Algorithm

AES (Advanced Encryption Standard) is one of the most common and secured symmetric key cryptographic algorithms. AES has received considerable attention from scientists in latest years due to its broad spectrum of applications such as communication, military, network, electronic banking, Internet of Things (IoT), etc. AES can be implemented using software and hardware. Hardware implementation can be based on Field Programmable Gate Array (FPGA). By using hardware, a higher data rate for fast applications such as routers can be achieved compared to software implementation. In this paper we present an FPGA implementation for AES-192. We employ loop-unrolling, fully pipelining, and sub-pipelining techniques and other efficient methods for the most complex parts of AES-192 such as Mix-columns, S-boxes. Our AES-192 implementation using Xilinx Defense-Grade Virtex-7(XQ7VX330T-RF1157) FPGA achieves high throughput of 54.52 Gbps and maximum operational frequency of 425.996 MHZ.
SAR Image Fusion Based on Mathematical Frame

In this paper, we proposed a frame-based approach to synthetic aperture radar (SAR) images fusion. Specifically, the method consists two parts: image fusion preprocessing and fusion reconstruction. Firstly, we fuse a set of low-resolution (LR) SAR images to form an aligned coarse SAR image which still has image blur caused by the point spread function (PSF) by fusion preprocessing. Then Frame fundamental iterative regularization is used to reconstruct high-resolution (HR) SAR image. Both simulated and real SAR images are used to verify the validity of the method. The results show that details and definition of the LR image processed with the method are effectively improved.
Prediction of Frequent Out-Of-Hours’ Medical Use

The thesis of this research is that through the mining of Electronic Medical Records containing mixed types of data, and extracting patterns from the processed data, patients can be successfully categorised through means of supervised machine learning early in their engagement with health care providers. This categorisation has quite narrow parameters: the aim of which is identify patients that are less suitable to the health care provider being examined in the course of this research; specifically that of an out-of-hours health care cooperative (OOHC). The motivation for this is to provide potential means for interventionist healthcare, in line with the increasingly role that decentralised regional programmes are having in the avenues of treatment available for patients [19] and the increasing emphasis on community based intervention [24]. The patients in question are frequent users of the OOHC, and represent a small cohort within the dataset as a whole. Our classification methodology, based upon recurrent neural networks, achieves an Area Under the Curve of between 0.81 and 0.92 in the identification of these patients.
Computing Skills Beyond R for Fisheries Students

FisheriesVolume 45, Issue 3 p. 116-120 Column: Student Angle Computing Skills Beyond R for Fisheries Students Caleb A. Aldridge, Corresponding Author caa134@msstate.edu orcid.org/0000-0002-7655-9535 Mississippi State University, Department of Wildlife, Fisheries, and Aquaculture Mississippi Cooperative Fish and Wildlife Research Unit, Mail Stop 9690, Mississippi State, MS, 39762Search for more papers by this author Caleb A. Aldridge, Corresponding Author caa134@msstate.edu orcid.org/0000-0002-7655-9535 Mississippi State University, Department of Wildlife, Fisheries, and Aquaculture Mississippi Cooperative Fish and Wildlife Research Unit, Mail Stop 9690, Mississippi State, MS, 39762Search for more papers by this author First published: 25 March 2020 https://doi.org/10.1002/fsh.10392Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume45, Issue3March 2020Pages 116-120 RelatedInformation
A conflict‐free replicated data type for collaborative annotation systems

Summary With the advent of Web 2.0, numerous collaborative annotation systems have been developed in an effort to enable distant users to annotate the same multimedia resources such as texts, audio, images, and videos. However, the existing systems do not support the semantic aspect of the data available on the Web and ignore the convergence aspect when executing concurrent annotations. Based on the technologies of Semantic Web, this article presents a new conflict‐free replicated data type called OAC‐Set, which extends Open Annotation Collaboration data model to enable concurrent annotations while guaranteeing convergence, causality, and intention preservation criteria. The experimental results show that our approach is efficient and effective.
Proposal of Interactive Home System Using Computer Graphics (CG) Characters

in recent years, systems that make speech dialogs with people have become widespread with the development of AI technology. There are inorganic speaker type and humanoid (robot) type as human interface of the voice dialogue system used at home. The speaker type etc. may be difficult to talk to because of the inorganic design, while the humanoid type may be difficult to talk to due to its appearance and users’ preference for characters.Therefore, in this paper, we propose a voice interactive home system using CG characters that can freely change its appearance according to the user’s request. Specifically, when a human sensor detects a person, the CG character begins talking to increase the chance of conversation with the user. The system can also remotely operate various home appliances in the house with ECHONET-Lite through the CG character by voice dialogue, and also provide life information provision service such as weather and time. In this study, we considered a spoken dialogue system that is easier to talk to than a humanoid type.