Reviewerly/exHarmony huggingface dataset:
A study of user’s preferences of public library in Jakarta, Indonesia

Abstract This paper represents the study of public library to accommodate the user’s activity in library. The study is aimed to investigate the standards of public library and how to accommodate user’s activity by providing the space based on user needs. This study was done with descriptive method. The result showed the most thing users do in public library that useful for designing a children’s library and public library base on user’s needs and their preferences.
Design of Hybrid Learning Mode in Higher Vocational Colleges Based on Cloud Platform

The rapid development of information technology and its in-depth application in various industries have brought about tremendous changes in people's life and learning methods. Students often arrange time before class to log in to the cloud class platform, and participate in discussions and interactions through learning micro-class videos, animations, courseware, documents, websites and other teaching resources provided by teachers to complete the learning tasks required by the learning task list. This study uses the social statistics software package SPSS22.0 to analyze the questionnaire. The validity analysis uses the principal component analysis method and the maximum variation method orthogonal rotation for confirmatory factor analysis, and forced extraction of four factors. Data shows that 53.1% of learners believe that online learning based on cloud platforms can improved learning Efficiency. The hybrid learning model based on the cloud platform can solve the time wasting problem in traditional classrooms and meet the needs of students' autonomous learning.
Digital Locker System for College or University Admissions Using Blockchain Technology

To avoid the submission of hard copies of documents by manually visiting the college and getting them verified from the college authorities, we are proposing a system that uses blockchain technology. By using the proposed system, students and college authorities can carry out the admission procedures from anywhere and at any time. As the students are provided with their academic documents from certain boards of education or universities, the documents that are issued from trusted institutions can be labelled as verified or authentic by making use of certain cryptographic digital signatures, proving their authenticity. Due to the decentralized storage of documents on blockchain fabric, the chances of hacking into the system reduce extremely, almost making it an impossible task. This system aims to provide a blockchain-based digital locker which can be used to store the documents required for university admission.
교사를 위한 AI 학습 콘텐츠 추천 및 제작 플랫폼에 관한 연구

This study utilizes AI to recommend customized learning content to reduce the burden of teacher's class content production, and conducts research on educational platforms that analyze students' insufficient learning capabilities and also recommend content for them.
Data Mining and Intelligent Matching Algorithm in Electric Power Terminology Database

Abstract The paper designs a method and system for automatic extraction of professional vocabulary in the electric power field based on data mining and matching algorithms. The system extracts professional vocabulary related to power marketing from the corpus in the field of power marketing, converts it into corresponding English and adds it to the professional vocabulary of power marketing. Then we will collect the relevant vocabulary of the power system and the corresponding translation and add it to the professional vocabulary of power marketing. The thesis establishes a two-level index structure for the professional vocabulary of electric power marketing. We search the professional vocabulary database of electric power marketing, the order of retrieval is electric power system vocabulary, general dictionary, electric power marketing field extraction vocabulary, and the search result is returned to the user after the retrieval. The experiment verified that the system facilitates the efficient retrieval of user vocabulary, and then realizes the automatic conversion of professional vocabulary in the marketing standardization design results, so as to improve the quality and efficiency of professional vocabulary conversion.
A Decentralized Self-Optimization Approach for Distributed Component-Based Software Systems

Self-optimization is the process in which a software system autonomously and continuously adapts itself based on environmental changes which negatively impact its overall performance. Most self-optimizing systems rely on an abstract model, with the majority relying on an abstract model representing the running software architecture to guide self-optimization decisions. However, existing approaches to self-optimization are centralized resulting in a single point of failure. This paper outlines a decentralized approach to self-optimization based on DARE, an architecture-based framework for providing component-based software systems with self-configuration, self-healing, and self-protection properties. We show how DARE services are extended to monitor and analyze the incoming message rate of components as well as how these services coordinate at runtime to self-optimize the controlled software system without losing any transactions. Experimental results demonstrate DARE's ability to achieve self-optimization at runtime and in a decentralized way.
A Lyapunov Guidance Vector Field Based Continuous Curvature Path Generation for Waypoint Following of UAVs

This paper generates a continuous curvature path for Unmanned Aerial Vehicles (UAVs) to converge to a straight-line path using the Lyapunov Guidance Vector Field (LGVF) approach. Further, a waypoint following method that utilizes the straight line convergence algorithm along with a switching technique to transit between two consecutive waypoint segments to find a near time-optimal and smooth path has been presented. Numerical examples demonstrate that the proposed algorithm is better in comparison to the other well-performing algorithms existing in the literature and the corresponding results have been included.
Application Structure Computer Big Data Technology in Internet Learning

Abstract The establishment of online learning platform through the Internet is a more popular way of learning. However, the existing Internet learning platform is still not intelligent enough, especially in data integration and analysis. Therefore, this paper proposes the application and research of computer BD(Big Data) based on structure in Internet learning. In this paper, the structure of computer BD and Internet learning methods for in-depth research and analysis, the existing problems in the Internet learning, combined with structural computer BDAT(Big Data Analysis Technology), established a new Internet learning BD analysis method. In view of the complex source of Internet learning data and large amount of invalid information, this paper optimizes and improves the traditional BD analysis data preprocessing method according to the characteristics of Internet students’ learning. Through the optimization and improvement of the method in this paper, the data analysis ability of the system is improved. In order to further verify the practical application effect of structural computer BD in Internet learning, this paper takes the freshmen of computer major of grade 2018 in a university as the research object of this experiment and carries out relevant investigation and research. According to the survey, 90.3% of students think that BD analysis can improve their mastery of knowledge in online learning. The analysis shows that the structure-based computer BD plays a good auxiliary role in students’ Internet learning.
Automatic Deep Learning-based Temporal Video Segmentation Framework

A novel automatic deep learning-based temporal video segmentation technique is proposed in this research paper. The considered approach uses machine and deep learning solutions to detect the video shot transitions in a movie sequence. First, a high-level image feature extraction is performed on all the video frames, by applying a pre-trained convolutional neural network. The values of the distances between any two feature vectors corresponding to successive frames are then computed and clustered automatically, in order to determine the visual content discontinuities. Video segmentation experiments and method comparisons are also described here.
An Improved BoxInst Model for Plane Instance Segmentation in Remote Sensing Images

When traditional instance segmentation model is applied to the plane target in the remote sensing image, high-frequency co-occurrence background is easy to appear, which affects the segmentation precision. To solve this problem, this paper proposes an improved BoxInst model for the plane segmentation in remote sensing images. The improved model suppress high-frequency co-occurrence background by introducing background constraint loss. Foreground constraint loss is proposed to avoid trivial solutions caused by the background constraint loss. The improved model is verified on the plane data set, and the average precision of the proposed model is improved by 6% compared with the original BoxInst model.
An optimization scheme for segmented-memory neural network

Capture of long-term dependencies is a core task in sequence learning, and imitating the way of human memorization is a promising orientation. The existing algorithms can fractionate the sequence into segments with fixed length according to a prior knowledge, but the segmentation depends on the context and is difficult to assign length before network training. Thus in this paper, we propose a variant of segmented-memory neural network which can segment the sequence into arbitrary lengths and then perform cascade via a binarized mask of memory slots. For optimization of the network, we deduce an optimal mask theoretically, and then apply it in a novel scheme based on a sparsity regularizer. In experiments, we conduct ablation analysis and evaluation on some algorithmic or classification tasks, several models including the proposed one optimized by using lasso regularizer are adopted for comparison. Both of the fixed- and variable-length sequences are tested, and the results in different criteria have demonstrated the superiority of our proposed method.
Applications of Remote Sensing and GIS in Geological Mapping, Mineral Prospecting and Groundwater Investigations in the Arabian-Nubian Shield: Cases from the Red Sea Hills of NE Sudan

This chapter covers the applications of remote sensing (RS) and geographic information system (GIS) in geological mapping, mineral prospecting and groundwater investigations in the Red Sea Hills (RSH) of north-east Sudan. RS and GIS-aided geological mapping was presented, and the various steps were thoroughly discussed, with examples of actual work conducted in Kadaweib area. The employment of RS-GIS techniques provided benefits during the field work in terms of reducing both field duration and expenses. The use of the enhanced satellite images as base maps assisted in the interpretation and delineation of the boundaries of the various lithological units. Visual interpretation of various enhanced imageries, petrographic investigations and on-screen type of digitization in the GIS environment facilitated the production of the geological map. The obtained map is of good quality and represents a real contribution to the understanding of the geology of the region. Mineral potentiality of the RSH of Sudan is well-known since the ancient times. Many RS-based geological studies disclosed the discovery of new mineral resources in the Nubian-Arabian Shield. This is exemplified by the gossan types of gold deposits, which were discovered merely recently in Ariab and Orshab Mining Co. licence areas making use of RS and GIS techniques. Likewise, the porphyry cooper of Jebel Oheir was discovered by GRAS using similar procedures. The current exploration activities carried by QMSD in Ohier mineralization are greatly supported by the use of RS and the creation of GIS-based geologic geodatabase. Scarcity of fresh water in the RSH region due to geologic as well as climatic factors is renowned. Special techniques for groundwater investigations in basement terrains were presented. Mapping of lithological and structural elements of an area is important when carrying out water harvesting or groundwater investigations. In this regard, lineament mapping may provide insights into the potentiality of groundwater accumulation and storage capacity of the water bearing formation in the basement rocks. Watershed mapping via DEM additionally provides information about the sustainability of groundwater supply.
Research into the Online and Offline Hybrid Teaching Mode in Colleges under the Era of “Internet +”

Based on the concept of student-centered, output-oriented and continuous improvement, the writer mainly studied the online and offline teaching mode in colleges under the Era of “Internet +”. The paper includes five sections: the first part is the introduction of the background; the second part tells the information superiority in the Era of “Internet+”; the third section discusses the overview of hybrid teaching; the fourth part is the teaching design of hybrid teaching mode; and the last part deals with evaluation of the teaching mode. By changing the teaching mode, it will be expected that the learning effect is promoted and more talented people can be trained for the social development.
Remote review practices of Institutional Review Boards during the COVID-19 pandemic in Korea: comparison with a Japanese study

The worldwide pandemic of the novel coronavirus disease 2019 (COVID-19) had an immediate impact on acceleration of the introduction of alternative methods of operation for Institutional Review Boards (IRBs) defined in ICH E6 (GCP) guidelines. This study was carried out to compare the operational status of IRBs in Korea during the COVID-19 outbreak with the results of a survey conducted in Japan during the same period. An online survey of IRBs was conducted in Korea from May 5 to July 31, 2020. A self-administered questionnaire about the profiles of IRB organization, impact of the COVID-19 outbreak on IRB operation, and status of using Information and Communication Technology (ICT) tools such as electronic document management systems and digital communication systems was distributed by e-mail to 169 IRBs in Korea. Responses were collected electronically via the online questionnaire survey. The survey results were then analyzed and compared with those from our previous survey in Japan containing the same questionnaire items, which was distributed to 520 IRBs. A total of 43 IRBs in Korea participated in the survey, and the results were compared with those from 97 Japanese IRBs. Although the local IRB has the largest share in both Japan and Korea, the frequency of review meetings and the impact of the COVID-19 outbreak were greater in Korea than in Japan. Both countries used ICT during the COVID-19 outbreak, but Korea was less concerned about ICT use compared to Japan.
Research on the study satisfaction of private college students in the new educational reform of China based on SPSS 22.0 statistical software

In order to study the comprehensive teaching effect of private colleges in the new educational reform., taking the undergraduates of S University in Guangdong as the research object., a self-designed questionnaire was used to investigate the study satisfaction of 2200 students in the school. SPSS 22.0 statistical software was used to process and analyze the data. The questions of the questionnaire were grouped according to the aspects they were involved in., and then descriptive statistical methods., one-way analysis of variance., multiple linear regression analysis., and post-event comparison were used for statistical analysis of the data. The purpose of this study is to explore the overall study satisfaction of students in private colleges., the differences of grades and major categories of students' study satisfaction. And it will predict the extent to affect students' study satisfaction by teachers' attitudes and other factors., analyzes the factors that affect students' study satisfaction and the reasons for the differences., and considers the ways and strategies to improve the study satisfaction of private college students.
Model-based Clustering with Covariates

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Computing highest density regions for continuous univariate distributions with known probability functions

We examine the problem of computing the highest density region (HDR) in a computational context where the user has access to a density function and quantile function for the distribution (e.g., in the statistical language R). We examine several common classes of continuous univariate distributions based on the shape of the density function; this includes monotone densities, quasi-concave and quasi-convex densities, and general multimodal densities. In each case we show how the user can compute the HDR from the quantile and density functions by framing the problem as a nonlinear optimisation problem. We implement these methods in R to obtain general functions to compute HDRs for classes of distributions, and for commonly used families of distributions. We compare our method to existing R packages for computing HDRs and we show that our method performs favourably in terms of both accuracy and average speed.
Research on Opinion Target Extraction and System Information Collection Analysis for Reviews

Abstract Though the study of opinion target extraction of movie review, this paper proposed a method for opinion target extraction of movie review based on deep learning, and analyzed its emotional polarity. Finally, the experimental results were displayed in a visual manner to help users judge the details of the movie content. In this paper, by establishing a bidirectional short-term and short-term memory network model, the opinion target extraction task is translated into a sequence labeling task, which improved the accuracy of opinion object extraction. This paper crawled and organized the Chinese movie review corpus from the Internet and the experimental results proved the effectiveness of the proposed method.
Automated Person Search in a video using Face based Person Re-identification system

Intelligent surveillance is an important computer vision task, that can be used in real-time to monitor public places like airport, temples, mall etc. It is essential, since person of interest involved in crime can be found and tracked using surveillance cameras. Manual monitoring of surveillance videos is time consuming and boring to watch 24x7. Hence computers should be automated to ease this task. Same person captured across multiple cameras are associated using features that are common. The process of enabling correspondence between similar images that are taken in different lighting, different background, different costume, different pose and different time is known as person re-identification. Various methods have been proposed by researchers to find a solution to person re-identification problem. The main objective is to generate robust discriminant features from the entire human body images of the same person with varying pose, background and illumination. The results are further refined using facial key points of the face detected from the matched images. Deep Learning layers are customised to meet the objective. The experiments are carried out using CUHK03, a benchmark dataset. The model is tested using a real-time data collected at SRM Institute of Technology. The results are compromising and screenshots are included.
Multi-objective Tabu-based Differential Evolution for Teleportation of Smart Virtual Machines in Private Computing Clouds

We propose a multi-objective approach for using differential evolution algorithm with tabu search algorithm as an additional mutation for live migration (teleportation) of virtual machines. This issue is crucial in private computing clouds. Teleportation of virtual machines is supposed to be planned to determine Pareto-optimal solutions for several criteria such as workload of the bottleneck host, communication capacity of the critical node, electric power of hosts, and computer costs. Some numerical results are presented for the experimental cloud based on OpenStack platform for smart city and smart education systems.
Identifying and Rectifying Rational Gaps in Fuzzy Rule Based Systems for Regression Problems

Fuzzy Rule Based Systems (FRBSs) can suffer from incomplete and sparse rule bases as a result of selecting a small number of rules from a large universe of potential rules. This may lead to rational gaps creeping into the input output mapping, where sometimes, strongly correlated inputs displaying a linear relationship with the output do not exhibit the same behaviour during inferencing. This paper proposes a technique for identifying and rectifying such gaps for FRBSs using incomplete rule bases in real-world regression problems.
WITHDRAWN: A privacy-preserving authentication scheme based on Elliptic Curve Cryptography and using Quotient Filter in VANET

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3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs

UAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm.
Social and psychological mechanisms of investigative media text influence on development of recipient’s aesthetic preferences

The paper is devoted to the problems of the investigating media text influence on the semantic, ethical, aesthetical, and spiritual benchmarks of the recipients. The investigator’s effective tools of form and text creation are presented. Emphasis is placed on meaningful techniques focused on meeting the needs and demands of different social groups, dissemination of investigative information, its rhythm and scale, and are determined by the nature of development and functioning of various social systems. It has been proved that the content of investigative materials is formed, taking into account the features and patterns of psychological influence in the range from the usual informing to edification, persuasion, and suggestion.