Reviewerly/exHarmony huggingface dataset:
Using knowledge Graphs to Enhance the Interpretability of Clinical Decision Support Model

Current clinical practice relies heavily on technology to support decision-making. In particular, machine learning is increasingly used in decision support systems. This can be attributed to information overload, a fact that clinicians cannot consider all available information. The disadvantage of this method is that this kind of Clinical Decision Support Systems (CDSSs) is usually a black box, and it can’t understand its decision-making reasons. However, in a healthcare environment, trust and accountability are important issues, and such systems should best be interpretable. In contrast, other areas rely almost entirely on observational or subjective patient reported questionnaires to quantify medical conditions. Developers need to use cognitive science based Human-Computer Interaction (HCI) research methods to design practice models, including user-centered iterative design and common standards. The main work of this paper is to propose a clinical decision support model with enhanced interpretability, including an automated interface generation engine. In the design of personalized decision-making, enhance the universality of decision-making push. The clinical evidence is input and displayed in the form of tables, and the medical concepts and their matching with SNOMED CT terms, consistent navigation, and finally displayed in the form of knowledge spectrum. Enhance the flexibility of interaction and integrate workflow seamlessly. As a result, domain experts can get advice quickly and take appropriate actions at convenient points in the workflow without additional effort or delay. Optimizing the interaction and availability of CDSS with providers can enhance the use of CDSS. The iterative design of CDSS improves the usability of the system and the user’s popularity score. Our analysis shows that modern machine learning methods can provide interpretability compatible with domain Interpretation Knowledge Base (IKB) and traditional method ranking. Future work should focus on replicating these findings in other datasets and further testing different interpretable methods.
Exemplar Guided Latent Pre-trained Dialogue Generation

Pre-trained models with latent variables have been proved to be an effective method in the diverse dialogue generation. However, the latent variables in current models are finite and uninformative, making the generated responses lack diversity and informativeness. In order to address this problem, we propose an exemplar guided latent pre-trained dialogue generation model to sample the latent variables from a continuous sentence embedding space, which can be controlled by the exemplar sentences. The proposed model contains two parts: exemplar seeking and response generation. First, the exemplar seeking builds a sentence graph based on the given dataset and seeks an enlightened exemplar from the graph. Next, the response generation constructs informative latent variables based on the exemplar and generates diverse responses with latent variables. Experiments show that the model can effectively improve the propriety and diversity of responses and achieve state-of-the-art performance.
Retraction: Neural Networks for Tea Leaf Classification (Journal of Physics: Conference Series 1432 012075)

Published 15 September 2020 This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.
A Comparative Study on Construction of 3D Objects from 2D Images

The 3D image construction for the 2D images is a longstanding problem, explored by the number of computer graphics, computer vision, and machine learning research organizations from decades. After the evolution of deep learning architectures, scholars have shown their interest in developing 3D images from the 2D greyscale or RGB images because this perspective shows a significant influence in the discipline of computer vision. The applications of this conversion found in various fields of medical image analysis, robotic vision, game design, lunar explorations, 3D modelling, military, geographical structuring, physics, support models, etc. Potentially, when a 2D image converted to 3D representation, the same image can be viewed from different angles and directions. The 3D structure, which in turn generated, is much more informative than 2D images as it contains information about distance from the camera to a particular object. In this paper, we discussed various exiting methods for generating 3D representations from 2D images, using 3D representational data as well as without 3D representational data and proposing a novel approach for the construction of 3D models from existing 2D images using GAN. Generative adversarial networks (GANs) have shown tremendous results in generating new fake data from existing data, where we cannot detect the false data. Various other architectures of GAN, like HOLO-GAN, IG-GAN, have also been proposed to meet the need to convert 2D to 3D representation, which produced excellent results. After analysing, we provide an extensive comparative review on methods and architectures, which can convert 2D images to 3D objects and express our thoughts on the ideas proposed. Further, the concept of GAN extended to represent 360 view images, panorama images in 3D structures, which plays a vital role in spherical view analysis and synthesis, virtual reality design, augmented reality design, 3D modelling of data, etc.
Optimization of Database Security Mechanism in the Supporting Platform of Distance Education

Starting from the basic concept of cloud computing, this paper discusses the basic functional architecture and service model of cloud computing, expounds the basic status quo of cloud computing application in education and teaching, analyzes the problems existing in the application of cloud computing in Guangxi Radio and TV university system distance teaching, puts forward corresponding solutions, and explores the reform of distance teaching mode of law courses. It is of great significance to accelerate the application of cloud computing in other distance education courses.
Model reproduction of non-equilibrium thermodynamics principles as a means to provide software self-development

Abstract Further evolution of technologies in material sciences, hardware and software demands reproduction of self-organization as a manifestation of non-equilibrium thermodynamics principles in new artificial environment. This is especially actual for software engineering, where reproduction of non-equilibrium thermodynamics principles will allow enhancing speed and quality of development process. In this article we reveal, list and analyze principles to be reproduced in artificial systems to provide capabilities for self-development, self-healing and self-improvement. It is shown that self-development process is impossible without reproducing the metaphor of electric charge with its abilities for attraction and repulsion. But this metaphor is usually avoided in computer models. We propose and consider a method to reproduce the metaphor of electric charge in computer models by means of UNIX/Linux operating system tools. The obtained results provide the basis to establish an artificial physics, a new research area dedicated to adequate reproduction of principles of physics in the artificial environment of computer models.
Who is Liable for Non-Compliant Cryptocurrency Transactions: Why Not Hang a Bitcoin Miner Now and Then?

Cryptocurrencies are based on digitally signed updates to a ledger maintained in a decentralized manner according to consensus mechanisms in compliance with consented protocols. The ledger updates are often referred to as transactions and the participants in the consensus mechanisms can be called transaction validators. This paper analyzes whether holding transaction validators liable has a deterrent effect on non-compliant transactions and possible legal bases for enforcing such liability. It is argued that such a liability has a deterrent effect and seems indispensable in a world of mainstream cryptocurrency adoption.
Research of Advertisement performance measure system based on Apache Flink and AB testing

Nowadays due to the rapid growth of the internet, more and more websites or online applications are created to help people live a more convenient life. The main revenue source for these websites and online applications are through advertisements revenue. It has been a really hot topic recently regarding how to increase advertisements revenue through a better Ads designs that are more attractive to users. This paper researched on a real time Ads performance measure system based on apache flink that can effectively measure Ads performance 15 minutes after the Ads started. We also implemented this system in the paper as well.
Visualization concept for maintenance information using wearable devices

Purpose The purpose of this paper is to show the current state of methods for information visualization on wearable devices and demonstrate how these devices and methods can be used to support the work of aircraft maintenance engineers. This paper develops a path to make aircraft maintenance tasks more efficient and safe by providing relevant and important information to maintenance personnel matching the task they are currently working on. Design/methodology/approach The current researchers elaborate prerequisites that wearable computing devices have to meet to use them in aircraft maintenance. Based on these prerequisites the researchers evaluate such existing devices and visualization techniques with regard to how they can be used to visualize information in aircraft maintenance processes. Findings This paper elaborates a roadmap how wearable devices can be applied in future aircraft maintenance scenarios. It also shows that augmented reality (AR) is a technology that shows great potential to support aircraft maintenance staff. The current researchers develop a scenario, how AR applications on wearable devices support such tasks. Practical implications The implementation of the maintenance scenario that is developed in this paper will vastly reduce the possibilities of mistakes by maintenance personnel. This leads to more efficient maintenance processes and further increases the safety of aviation in general. Originality/value Although AR today already is used in fields of application like gaming, an application of this technology in a safety-critical environment is not known to the author. This paper provides a step towards extending the application domains of this technology to aircraft maintenance.
Developing novel quantitative imaging analysis schemes based machine learning for cancer research

The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of developing several new novel CAD schemes for different cancer research purposes. First, I investigated the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to predict short-term breast cancer risk. For this study, an existing CAD scheme was applied “as is” to process each image. From CAD-generated results, some detection features were computed from each image. Two logistic regression models were then trained and tested using a leave-one-case-out cross-validation method to predict each testing case's likelihood of being positive in the next subsequent screening. This study demonstrated that CAD-generated false-positives contain valuable information to predict short-term breast cancer risk. Second, I identified and applied quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. For this study, a CAD scheme was developed to perform tumor segmentation and image feature analysis. The study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies. Last, I optimized a machine learning model for predicting peritoneal metastasis in gastric cancer. For this purpose, I have developed a CAD scheme to segment the tumor volume and extract quantitative image features automatically. Then, I reduced the dimensionality of features with a new method named random projection to optimize the model's performance. Finally, the gradient boosting machine model was applied along with a synthetic minority oversampling technique to predict peritoneal metastasis risk. Results suggested that the random projection method yielded promising results in improving the accuracy performance in peritoneal metastasis prediction.In summary, in my Ph.D. studies, I have investigated and tested several innovative approaches to develop different CAD schemes and identify quantitative imaging markers with high discriminatory power in various cancer research applications. Study results demonstrated the feasibility of applying CAD technology to several new application fields, which can help radiologists and gynecologists improve accuracy and consistency in disease diagnosis and prognosis assessment of using the medical image.
The influence of linear feedback control suppression under the calculation of fractional financial system on the upgrading of high-tech industries

<abstract> The paper uses the panel smooth conversion fractional-order system model to divide the mechanism of financial development in high-tech industries into two systems. At the same time, the article uses a continuous state to connect these two extreme situations to investigate the nonlinear characteristics of linear feedback control that inhibit the impact of financial development on high-tech industries. Studies have shown that financial development under a low system has a significant role in promoting high-tech industries. On the other hand, financial development under the high-level system has a restraining effect on high-tech industries. There are dual-threshold characteristics between the scale of financial development and the development of high-tech industries. Firstly, the scale of financial development should be controlled within [0.553, 0.840]. Secondly, there is a single threshold between the level of financial development and the development of high-tech industries, and the level of financial development should be controlled within 0.756. </abstract>
Application of Concrete Filled Steel Tubes in Solar Module Mounting Structure

Solar energy is a renewable form of energy. Sustainable development requires the promotion of renewable sources of energy as much as possible. Solar energy can be used to generate the electric energy by using a photovoltaic system, which could convert sunlight into electricity. Solar panels are arranged in a solar module mounting structure made of steel. The tracking of the solar panel is facilitated by the linear actuators. The solar module mounting structure is subjected to various different types of loading. Wind loading is a major concern for the structural integrity and stability of the module mounting structure. The solar module mounting structure is analyzed for various loads using the STAAD PRO structural analysis software, and then the results are used by ABAQUS finite element software to compare the behavior of hollow steel torque tube and concrete filled steel torque tubes under flexural and torsional stresses.
Modern professional educator: conditions and factors of its formation

The article analyzes the view of a number of Russian scientists (V. A. Slastenin, A. Ya. Savelyev, A. A. Verbitsky etc.) on the problem of pedagogical professionalism as a system of stable personal traits, ensuring high productivity and effciency of work aimed to form individual students competencies. The external and internal factors for the formation of a modern professional teacher are also determined: state and social orders for a competent teacher, an educator, a teacher of a higher institution, their personal characteristics and desire to develop. It is noted that as a result of self-development, teachers realize the needs for the development of such personal qualities and competencies, which guarantee them high results in their professional activities and success in life.
Science and Technology for Community: Improving Web Programming Skills using Laravel Framework

The essence of technology is to help human work become easier, cost effective, save time more efficiently and on target. The use of technology that is studded with innovation provides real changes in every activity, including in the world of education. Even in some countries such as Australia and Hong Kong, technology has been introduced through basic programming since the age of 3 years. In Indonesia in 2016 the Minister of Communication and Information, Rudiantara, has discussed programming so that it is included in the elementary school curriculum. The introduction of technology at the elementary school level aims to prepare young people who are ready to face technology in the industrial era 4.0. This service activity is carried out in order to improve web programming capabilities using the Laravel framework. The hope is that by providing the ability to program using the Laravel framework, it can support academic education and increase employment opportunities with more capabilities.
Use of a non-peer reviewed sources in cyber-security scientific research

Most publicly available data on cyber incidents comes from private companies and non-academic sources. Common sources of information include various security bulletins, white papers, reports, court cases, and blog posts describing specific events, often from a single point of view, followed by occasional academic sources, usually conference proceedings. The main characteristics of the available data sources are: lack of peer review and unavailability of confidential data. In this paper, we use an indirect approach to identify trusted sources used in scientific work. We analyze how top-rated peer reviewed literature relies on the use of non-peer reviewed sources on cybersecurity incidents. To identify current non-peer reviewed sources on cybersecurity we analyze references in top rated peer reviewed computer security conferences. We also analyze how non-peer reviewed sources are used, to motivate or support research. We examined 808 articles from top conferences in field of computer security. The result of this work are list of the most commonly used non-peer reviewed data sources and information about the context in which this data is used. Since these sources are accepted in top conferences, other researchers can consider them in their future research. To the best of our knowledge, analysis on how non-peer reviewed sources are used in cybersecurity scientific research has not been done before.
Using the grade-rating system assessing the knowledge when teaching informational and legal disciplines in universities of law enforcement agencies

This article discusses the practical experience of using the grade-rating system for assessing the knowledge of cadets in educational organizations within the Ministry of Internal Affairs of Russia when using the Moodle virtual learning environment. The advantages and disadvantages of the grade-rating system are analyzed.
Automatic Re-modularization of Clustered Codes Considering Invocation Types

One of the approaches for software re-modularization is to transform sequential codes into distributed ones. Code clustering as a technique for software re-modularization is used on object-oriented codes. By clustering, the classes in a program are separated into several clusters, each of which contains cohesive classes. In the state of the art, the type of invocation or message passing between classes and objects is not considered in the clustering, which can impact the quality of the clusters. This paper presents an approach in which the types of invocations between classes and objects such as synchronous, one-way, or asynchronous will also be considered in the clustering. The aim is to separate asynchronous and one-way services from the clusters and move them to new clusters, automatically. So that, by running these services in parallel, extra waiting times for service invocations can be eliminated. Also, by transferring these services to new clusters, load distribution has taken place, which can reduce response time. The experiment results confirm that the proposed approach has a significant effect in reducing the overall waiting time.
Container Scheduling in Blockchain-based Cloud Service Platform

When massive mobile and Internet of Things applications emerge, the cloud computing models relying on traditional centralized data centers will encounter many constraints. Unlike traditional centralized cloud computing, Mobile Edge Computing integrates cloud computing, mobile computing and wireless network functions, enabling Cloud to handle large amounts of storage and computing problems of mobile devices in real time. We established a Blockchain-based service platform for QoS and QoE in mobile edge cloud ecosystem. The platform user can invoke all API services in the platform without redirecting to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are Real-world services. In addition, data collected from the platform can be stored on Blockchain, and smart contract is proposed for assuring the QoS and QoE.
Examination of Positive Learning Experiences in Small Elementary Schools

This study aims to examine positive learning experiences in small elementary schools and suggest support plans for improving circumstances and quality of learning experiences in small elementary schools. First, it reviewed previous studies on small schools. Second, it analyzed positive learning experiences in small elementary schools. Finally, it suggested support plans for revitalization and quality education of small elementary schools. In order to explore students’ learning experiences in small elementary schools, the authors observed classes of six small elementary schools, interviewed teachers and students, and analyzed related documents of school curriculum. The study presented three positive distinctions of learning experiences in small elementary schools: first, friendly and immediate communication between teachers and students; second, learning connected with local community; and third, plenty of cultural experience learning in and out of the country. As the support plans, it suggested that support for implementing grade clusters, sharing good practices of small schools, and developing legal foundation for revitalization of small schools are necessary.